Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

Abstract: Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD matrices has proven key to the success of many algorithms. However, most existing methods only approximate the true shape of the manifold locally by its tangent plane. In this paper, inspired by kernel methods, we propose to map SPD matrices to a high dimensional Hilbert space where Euclidean geometry applies. To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices. These kernels are derived from the Gaussian kernel, but exploit different metrics on the manifold. This lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM and kernel PCA, to the Riemannian manifold of SPD matrices. We demonstrate the benefits of our approach on the problems of pedestrian detection, object categorization, texture analysis, 2D motion segmentation and Diffusion Tensor Imaging (DTI) segmentation.

@InProceedings{Jayasumana_2013_ICCV_Workshops,author = {Jayasumana, Sadeep and Hartley, Richard and Salzmann, Mathieu and Li, Hongdong and Harandi, Mehrtash},title = {Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: The proposed approach is based on standard graph transduction, semi-supervised learning (SSL) framework. Its key novelty is the integration of global connectivity constraints into this framework. Although connectivity leads to higher order constraints and their number is an exponential, finding the most violated connectivity constraint can be done efficiently in polynomial time. Moreover, each such constraint can be represented as a linear inequality. Based on this fact, we design a cutting-plane algorithm to solve the integrated problem. It iterates between solving a convex quadratic problem of label propagation with linear inequality constraints, and finding the most violated constraint. We demonstrate the benefits of the proposed approach on a realistic and very challenging problem of cosegmentation of multiple foreground objects in photo collections in which the foreground objects are not present in all photos. The obtained results not only demonstrate performance boost induced by the connectivity constraints, but also show a significant improvement over the state-of-the-art methods.

@InProceedings{Ma_2013_ICCV_Workshops,author = {Ma, Tianyang and Jan Latecki, Longin},title = {Graph Transduction Learning with Connectivity Constraints with Application to Multiple Foreground Cosegmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We propose Max-Margin Riffled Independence Model (MMRIM), a new method for image tag ranking modeling the structured preferences among tags. The goal is to predict a ranked tag list for a given image, where tags are ordered by their importance or relevance to the image content. Our model integrates the max-margin formalism with riffled independence factorizations proposed in [10], which naturally allows for structured learning and efficient ranking. Experimental results on the SUN Attribute and LabelMe datasets demonstrate the superior performance of the proposed model compared with baseline tag ranking methods. We also apply the predicted rank list of tags to several higher-level computer vision applications in image understanding and retrieval, and demonstrate that MMRIM significantly improves the accuracy of these applications.

@InProceedings{Lan_2013_ICCV_Workshops,author = {Lan, Tian and Mori, Greg},title = {A Max-Margin Riffled Independence Model for Image Tag Ranking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Recent years have witnessed a growing interest in understanding the semantics of point clouds in a wide variety of applications. However, point cloud labeling remains an open problem, due to the difficulty in acquiring sufficient 3D point labels towards training effective classifiers. In this paper, we overcome this challenge by utilizing the existing massive 2D semantic labeled datasets from decadelong community efforts, such as ImageNet and LabelMe, and a novel "cross-domain" label propagation approach. Our proposed method consists of two major novel components, Exemplar SVM based label propagation, which effectively addresses the cross-domain issue, and a graphical model based contextual refinement incorporating 3D constraints. Most importantly, the entire process does not require any training data from the target scenes, also with good scalability towards large scale applications. We evaluate our approach on the well-known Cornell Point Cloud Dataset, achieving much greater efficiency and comparable accuracy even without any 3D training data. Our approach shows further major gains in accuracy when the training data from the target scenes is used, outperforming state-ofthe-art approaches with far better efficiency.

@InProceedings{Wang_2013_ICCV_Workshops,author = {Wang, Yan and Ji, Rongrong and Chang, Shih-Fu},title = {Label Propagation from ImageNet to 3D Point Clouds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: This paper proposes a new supervised semantic edge and gradient extraction approach, which allows the user to roughly scribble over the desired region to extract semantically-dominant and coherent edges in it. Our approach first extracts low-level edgelets (small edge clusters) from the input image as primitives and build a graph upon them, by jointly considering both the geometric and appearance compatibility of edgelets. Given the characteristics of the graph, it cannot be effectively optimized by commonly-used energy minimization tools such as graph cuts. We thus propose an efficient linear algorithm for precise graph optimization, by taking advantage of the special structure of the graph. Objective evaluations show that the proposed method significantly outperforms previous semantic edge detection algorithms. Finally, we demonstrate the effectiveness of the system in various image editing tasks.

@InProceedings{Yang_2013_ICCV_Workshops,author = {Yang, Shulin and Wang, Jue and Shapiro, Linda},title = {Supervised Semantic Gradient Extraction Using Linear-Time Optimization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Martin Bauml, Makarand Tapaswi, Rainer Stiefelhagen

Abstract: We address the problem of person identification in TV series. We propose a unified learning framework for multiclass classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.

@InProceedings{Bauml_2013_ICCV_Workshops,author = {Bauml, Martin and Tapaswi, Makarand and Stiefelhagen, Rainer},title = {Semi-supervised Learning with Constraints for Person Identification in Multimedia Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Vittal Premachandran, Ramakrishna Kakarala

Abstract: Propagating similarity information along the data manifold requires careful selection of local neighborhood. Selecting a "good" neighborhood in an unsupervised setting, given an affinity graph, has been a difficult task. The most common way to select a local neighborhood has been to use the k-nearest neighborhood (k-NN) selection criterion. However, it has the tendency to include noisy edges. In this paper, we propose a way to select a robust neighborhood using the consensus of multiple rounds of k-NNs. We explain how using consensus information can give better control over neighborhood selection. We also explain in detail the problems with another recently proposed neighborhood selection criteria, i.e., Dominant Neighbors, and show that our method is immune to those problems. Finally, we show the results from experiments in which we compare our method to other neighborhood selection approaches. The results corroborate our claims that consensus of k-NNs does indeed help in selecting more robust and stable localities.

@InProceedings{Premachandran_2013_ICCV_Workshops,author = {Premachandran, Vittal and Kakarala, Ramakrishna},title = {Consensus of k-NNs for Robust Neighborhood Selection on Graph-Based Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Fei Chen, Huimin Yu, Roland Hu, Xunxun Zeng

Abstract: In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation. Experiments demonstrate that our model can be applied to dataset of arbitrary prior shapes, and can cope with image noise and clutter, as well as partial occlusions.

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Fei and Yu, Huimin and Hu, Roland and Zeng, Xunxun},title = {Deep Learning Shape Priors for Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Recently, the Counting Grid (CG) model [5] was developed to represent each input image as a point in a large grid of feature counts. This latent point is a corner of a window of grid points which are all uniformly combined to match the (normalized) feature counts in the image. Being a bag of word model with spatial layout in the latent space, the CG model has superior handling of field of view changes in comparison to other bag of word models, but with the price of being essentially a mixture, mapping each scene to a single window in the grid. In this paper we introduce a family of componential models, dubbed the Componential Counting Grid, whose members represent each input image by multiple latent locations, rather than just one. In this way, we make a substantially more flexible admixture model which captures layers or parts of images and maps them to separate windows in a Counting Grid. We tested the models on scene and place classification where their componential nature helped to extract objects, to capture parallax effects, thus better fitting the data and outperforming Counting Grids and Latent Dirichlet Allocation, especially on sequences taken with wearable cameras.

@InProceedings{Perina_2013_ICCV_Workshops,author = {Perina, Alessandro and Jojic, Nebojsa},title = {Capturing Layers in Image Collections with Componential Models: From the Layered Epitome to the Componential Counting Grid},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: The recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. The quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. The uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. The key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. The depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.

@InProceedings{Shen_2013_ICCV_Workshops,author = {Shen, Ju and Cheung, Sen-Ching S.},title = {Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Kaifu Yang, Shaobing Gao, Chaoyi Li, Yongjie Li

Abstract: Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas. In this study, we propose a new framework for boundary detection in complex natural scenes based on the color-opponent mechanisms of the visual system. The red-green and blue-yellow color opponent channels in the human visual system are regarded as the building blocks for various color perception tasks such as boundary detection. The proposed framework is a feedforward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to the primary visual cortex (V1). Results show that our simple framework has excellent ability to flexibly capture both the structured chromatic and achromatic boundaries in complex scenes.

@InProceedings{Yang_2013_ICCV_Workshops,author = {Yang, Kaifu and Gao, Shaobing and Li, Chaoyi and Li, Yongjie},title = {Efficient Color Boundary Detection with Color-Opponent Mechanisms},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Adaptive Compressed Tomography Sensing

Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel

Abstract: One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image. We propose a mathematical model for adaptive CT acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, non-adaptive acquisition algorithms.

@InProceedings{Barkan_2013_ICCV_Workshops,author = {Barkan, Oren and Weill, Jonathan and Averbuch, Amir and Dekel, Shai},title = {Adaptive Compressed Tomography Sensing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos

Abstract: The problem of action recognition and human activity has been an active research area in Computer Vision and Robotics. While full-body motions can be characterized by movement and change of posture, no characterization, that holds invariance, has yet been proposed for the description of manipulation actions. We propose that a fundamental concept in understanding such actions, are the consequences of actions. There is a small set of fundamental primitive action consequences that provides a systematic high-level classification of manipulation actions. In this paper a technique is developed to recognize these action consequences. At the heart of the technique lies a novel active tracking and segmentation method that monitors the changes in appearance and topological structure of the manipulated object. These are then used in a visual semantic graph (VSG) based procedure applied to the time sequence of the monitored object to recognize the action consequence. We provide a new dataset, called Manipulation Action Consequences (MAC 1.0), which can serve as testbed for other studies on this topic. Several experiments on this dataset demonstrates that our method can robustly track objects and detect their deformations and division during the manipulation. Quantitative tests prove the effectiveness and efficiency of the method.

@InProceedings{Yang_2013_ICCV_Workshops,author = {Yang, Yezhou and Fermuller, Cornelia and Aloimonos, Yiannis},title = {Detection of Manipulation Action Consequences (MAC)},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Mihir Jain, Herve Jegou, Patrick Bouthemy

Abstract: Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description. This paper establishes that adequately decomposing visual motion into dominant and residual motions, both in the extraction of the space-time trajectories and for the computation of descriptors, significantly improves action recognition algorithms. Then, we design a new motion descriptor, the DCS descriptor, based on differential motion scalar quantities, divergence, curl and shear features. It captures additional information on the local motion patterns enhancing results. Finally, applying the recent VLAD coding technique proposed in image retrieval provides a substantial improvement for action recognition. Our three contributions are complementary and lead to outperform all reported results by a significant margin on three challenging datasets, namely Hollywood 2, HMDB51 and Olympic Sports.

@InProceedings{Jain_2013_ICCV_Workshops,author = {Jain, Mihir and Jegou, Herve and Bouthemy, Patrick},title = {Better Exploiting Motion for Better Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Constraints as Features

Abstract: In this paper, we introduce a new approach to constrained clustering which treats the constraints as features. Our method augments the original feature space with additional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering algorithm can be performed, like k-means or spectral clustering. We demonstrate the efficacy of our method for active semi-supervised learning applied to image segmentation and compare it to alternative methods. We also evaluate the performance of our method on the four most commonly evaluated datasets from the UCI machine learning repository.

@InProceedings{Asafi_2013_ICCV_Workshops,author = {Asafi, Shmuel and Cohen-Or, Daniel},title = {Constraints as Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We address the problem of recovering camera motion from video data, which does not require the establishment of feature correspondences or computation of optical flows but from normal flows directly. We have designed an imaging system that has a wide field of view by fixating a number of cameras together to form an approximate spherical eye. With a substantially widened visual field, we discover that estimating the directions of translation and rotation components of the motion separately are possible and particularly efficient. In addition, the inherent ambiguities between translation and rotation also disappear. Magnitude of rotation is recovered subsequently. Experimental results on synthetic and real image data are provided. The results show that not only the accuracy of motion estimation is comparable to those of the state-of-the-art methods that require explicit feature correspondences or optical flows, but also a faster computation time.

@InProceedings{Hui_2013_ICCV_Workshops,author = {Hui, Tak-Wai and Chung, Ronald},title = {Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Akihiko Torii, Josef Sivic, Tomas Pajdla, Masatoshi Okutomi

Abstract: Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are notoriously hard for establishing correspondences using multi-view geometry. Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance. In this work we show that repeated structures are not a nuisance but, when appropriately represented, they form an important distinguishing feature for many places. We describe a representation of repeated structures suitable for scalable retrieval. It is based on robust detection of repeated image structures and a simple modification of weights in the bag-of-visual-word model. Place recognition results are shown on datasets of street-level imagery from Pittsburgh and San Francisco demonstrating significant gains in recognition performance compared to the standard bag-of-visual-words baseline and more recently proposed burstiness weighting.

@InProceedings{Torii_2013_ICCV_Workshops,author = {Torii, Akihiko and Sivic, Josef and Pajdla, Tomas and Okutomi, Masatoshi},title = {Visual Place Recognition with Repetitive Structures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation. In many applications, both vector data X and graph data W are available. Laplacian embedding is widely used for embedding graph data. We propose a graph-Laplacian PCA (gLPCA) to learn a low dimensional representation of X that incorporates graph structures encoded in W . This model has several advantages: (1) It is a data representation model. (2) It has a compact closed-form solution and can be efficiently computed. (3) It is capable to remove corruptions. Extensive experiments on 8 datasets show promising results on image reconstruction and significant improvement on clustering and classification.

@InProceedings{Jiang_2013_ICCV_Workshops,author = {Jiang, Bo and Ding, Chris and Luo, Bio and Tang, Jin},title = {Graph-Laplacian PCA: Closed-Form Solution and Robustness},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by singleand multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public datasets: Caltech, TUD-Brussels and ETH. Experimental results show that our framework significantly improves all these approaches. The average improvement is 9% on the Caltech-Test dataset, aveaon the TUD-Brussels dataset and teh-on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 48% to 43% on the Caltech-Test dataset, from edueto fom4on the TUD-Brussels dataset and from etfoto 55%ton the ETH dataset.

@InProceedings{Ouyang_2013_ICCV_Workshops,author = {Ouyang, Wanli and Wang, Xiaogang},title = {Single-Pedestrian Detection Aided by Multi-pedestrian Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Luca Del Pero, Joshua Bowdish, Bonnie Kermgard, Emily Hartley, Kobus Barnard

Abstract: We develop a comprehensive Bayesian generative model for understanding indoor scenes. While it is common in this domain to approximate objects with 3D bounding boxes, we propose using strong representations with finer granularity. For example, we model a chair as a set of four legs, a seat and a backrest. We find that modeling detailed geometry improves recognition and reconstruction, and enables more refined use of appearance for scene understanding. We demonstrate this with a new likelihood function that rewards 3D object hypotheses whose 2D projection is more uniform in color distribution. Such a measure would be confused by background pixels if we used a bounding box to represent a concave object like a chair. Complex objects are modeled using a set or re-usable 3D parts, and we show that this representation captures much of the variation among object instances with relatively few parameters. We also designed specific data-driven inference mechanisms for each part that are shared by all objects containing that part, which helps make inference transparent to the modeler. Further, we show how to exploit contextual relationships to detect more objects, by, for example,proposing chairs around and underneath tables. We present results showing the benefits of each of these innovations. The performance of our approach often exceeds that of state-of-the-art methods on the two tasks of room layout estimation and object recognition, as evaluated on two bench mark data sets used in this domain.

@InProceedings{Pero_2013_ICCV_Workshops,author = {Del Pero, Luca and Bowdish, Joshua and Kermgard, Bonnie and Hartley, Emily and Barnard, Kobus},title = {Understanding Bayesian Rooms Using Composite 3D Object Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Stefan Harmeling, Michael Hirsch, Bernhard Scholkopf

Abstract: We establish a link between Fourier optics and a recent construction from the machine learning community termed the kernel mean map. Using the Fraunhofer approximation, it identifies the kernel with the squared Fourier transform of the aperture. This allows us to use results about the invertibility of the kernel mean map to provide a statement about the invertibility of Fraunhofer diffraction, showing that imaging processes with arbitrarily small apertures can in principle be invertible, i.e., do not lose information, provided the objects to be imaged satisfy a generic condition. A real world experiment shows that we can super-resolve beyond the Rayleigh limit.

@InProceedings{Harmeling_2013_ICCV_Workshops,author = {Harmeling, Stefan and Hirsch, Michael and Scholkopf, Bernhard},title = {On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi

Abstract: Background modeling and subtraction is an essential task in video surveillance applications. Most traditional studies use information observed in past frames to create and update a background model. To adapt to background changes, the background model has been enhanced by introducing various forms of information including spatial consistency and temporal tendency. In this paper, we propose a new framework that leverages information from a future period. Our proposed approach realizes a low-cost and highly accurate background model. The proposed framework is called bidirectional background modeling, and performs background subtraction based on bidirectional analysis; i.e., analysis from past to present and analysis from future to present. Although a result will be output with some delay because information is taken from a future period, our proposed approach improves the accuracy by about 30% if only a 33-millisecond of delay is acceptable. Furthermore, the memory cost can be reduced by about 65% relative to typical background modeling.

@InProceedings{Shimada_2013_ICCV_Workshops,author = {Shimada, Atsushi and Nagahara, Hajime and Taniguchi, Rin-ichiro},title = {Background Modeling Based on Bidirectional Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen

Abstract: Recently, groupwise registration has been investigated for simultaneous alignment of all images without selecting any individual image as the template, thus avoiding the potential bias in image registration. However, none of current groupwise registration method fully utilizes the image distribution to guide the registration. Thus, the registration performance usually suffers from large inter-subject variations across individual images. To solve this issue, we propose a novel groupwise registration algorithm for large population dataset, guided by the image distribution on the manifold. Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both synthetic and real datasets, with comparison to the two state-of-the-art groupwise registration methods. All experimental results show that our proposed method achieves the best performance in terms of registration accuracy and robustness.

@InProceedings{Ying_2013_ICCV_Workshops,author = {Ying, Shihui and Wu, Guorong and Wang, Qian and Shen, Dinggang},title = {Groupwise Registration via Graph Shrinkage on the Image Manifold},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We propose a novel tracking algorithm that robustly tracks the target by finding the state which minimizes uncertainty of the likelihood at current state. The uncertainty of the likelihood is estimated by obtaining the gap between the lower and upper bounds of the likelihood. By minimizing the gap between the two bounds, our method finds the confident and reliable state of the target. In the paper, the state that gives the Minimum Uncertainty Gap (MUG) between likelihood bounds is shown to be more reliable than the state which gives the maximum likelihood only, especially when there are severe illumination changes, occlusions, and pose variations. A rigorous derivation of the lower and upper bounds of the likelihood for the visual tracking problem is provided to address this issue. Additionally, an efficient inference algorithm using Interacting Markov Chain Monte Carlo is presented to find the best state that maximizes the average of the lower and upper bounds of the likelihood and minimizes the gap between two bounds simultaneously. Experimental results demonstrate that our method successfully tracks the target in realistic videos and outperforms conventional tracking methods.

@InProceedings{Kwon_2013_ICCV_Workshops,author = {Kwon, Junseok and Mu Lee, Kyoung},title = {Minimum Uncertainty Gap for Robust Visual Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Peng Ye, Jayant Kumar, Le Kang, David Doermann

Abstract: This paper addresses the problem of general-purpose No-Reference Image Quality Assessment (NR-IQA) with the goal of developing a real-time, cross-domain model that can predict the quality of distorted images without prior knowledge of non-distorted reference images and types of distortions present in these images. The contributions of our work are two-fold: first, the proposed method is highly efficient. NR-IQA measures are often used in real-time imaging or communication systems, therefore it is important to have a fast NR-IQA algorithm that can be used in these real-time applications. Second, the proposed method has the potential to be used in multiple image domains. Previous work on NR-IQA focus primarily on predicting quality of natural scene image with respect to human perception, yet, in other image domains, the final receiver of a digital image may not be a human. The proposed method consists of the following components: (1) a local feature extractor; (2) a global feature extractor and (3) a regression model. While previous approaches usually treat local feature extraction and regression model training independently, we propose a supervised method based on back-projection, which links the two steps by learning a compact set of filters which can be applied to local image patches to obtain discriminative local features. Using a small set of filters, the proposed method is extremely fast. We have tested this method on various natural scene and document image datasets and obtained stateof-the-art results.

@InProceedings{Ye_2013_ICCV_Workshops,author = {Ye, Peng and Kumar, Jayant and Kang, Le and Doermann, David},title = {Real-Time No-Reference Image Quality Assessment Based on Filter Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Sateesh Pedagadi, James Orwell, Sergio Velastin, Boghos Boghossian

Abstract: Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Furthermore, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (P UR ). This measure is invariant to test set size and provides an intuitive indication of performance.

@InProceedings{Pedagadi_2013_ICCV_Workshops,author = {Pedagadi, Sateesh and Orwell, James and Velastin, Sergio and Boghossian, Boghos},title = {Local Fisher Discriminant Analysis for Pedestrian Re-identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Aparna Taneja, Luca Ballan, Marc Pollefeys

Abstract: In this paper, we propose a method to detect changes in the geometry of a city using panoramic images captured by a car driving around the city. We designed our approach to account for all the challenges involved in a large scale application of change detection, such as, inaccuracies in the input geometry, errors in the geo-location data of the images, as well as, the limited amount of information due to sparse imagery. We evaluated our approach on an area of 6 square kilometers inside a city, using 3420 images downloaded from Google StreetView. These images besides being publicly available, are also a good example of panoramic images captured with a driving vehicle, and hence demonstrating all the possible challenges resulting from such an acquisition. We also quantitatively compared the performance of our approach with respect to a ground truth, as well as to prior work. This evaluation shows that our approach outperforms the current state of the art.

@InProceedings{Taneja_2013_ICCV_Workshops,author = {Taneja, Aparna and Ballan, Luca and Pollefeys, Marc},title = {City-Scale Change Detection in Cadastral 3D Models Using Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Bojan Pepikj, Michael Stark, Peter Gehler, Bernt Schiele

Abstract: Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of methods that treat occlusion as just another source of noise instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. These patterns are then used as training data for dedicated detectors of varying sophistication. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid further developments in tackling the occlusion challenge.

@InProceedings{Pepikj_2013_ICCV_Workshops,author = {Pepikj, Bojan and Stark, Michael and Gehler, Peter and Schiele, Bernt},title = {Occlusion Patterns for Object Class Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Xiao Liu, Mingli Song, Dacheng Tao, Zicheng Liu, Luming Zhang, Chun Chen, Jiajun Bu

Abstract: Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training data. In this paper, we present semi-supervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and unlabeled data. In particular, we derive a nonparametric algorithm to obtain an accurate quality measure of splitting by incorporating abundant unlabeled data. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. The proposed algorithm is compared with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization and image segmentation. Experimental results on publicly available datasets demonstrate the superiority of our method.

@InProceedings{Liu_2013_ICCV_Workshops,author = {Liu, Xiao and Song, Mingli and Tao, Dacheng and Liu, Zicheng and Zhang, Luming and Chen, Chun and Bu, Jiajun},title = {Semi-supervised Node Splitting for Random Forest Construction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Asma Rejeb Sfar, Nozha Boujemaa, Donald Geman

Abstract: We study fine-grained categorization, the task of distinguishing among (sub)categories of the same generic object class (e.g., birds), focusing on determining botanical species (leaves and orchids) from scanned images. The strategy is to focus attention around several vantage points, which is the approach taken by botanists, but using features dedicated to the individual categories. Our implementation of the strategy is based on vantage feature frames, a novel object representation consisting of two components: a set of coordinate systems centered at the most discriminating local viewpoints for the generic object class and a set of category-dependent features computed in these frames. The features are pooled over frames to build the classifier. Categorization then proceeds from coarse-grained (finding the frames) to fine-grained (finding the category), and hence the vantage feature frames must be both detectable and discriminating. The proposed method outperforms state-of-the art algorithms, in particular those using more distributed representations, on standard databases of leaves.

@InProceedings{Sfar_2013_ICCV_Workshops,author = {Rejeb Sfar, Asma and Boujemaa, Nozha and Geman, Donald},title = {Vantage Feature Frames for Fine-Grained Categorization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Jason Chang, Donglai Wei, John W. Fisher III

Abstract: We develop a generative probabilistic model for temporally consistent superpixels in video sequences. In contrast to supervoxel methods, object parts in different frames are tracked by the same temporal superpixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate superpixels in an online fashion. We consider four novel metrics to quantify performance of a temporal superpixel representation and demonstrate superior performance when compared to supervoxel methods.

@InProceedings{Chang_2013_ICCV_Workshops,author = {Chang, Jason and Wei, Donglai and Fisher, III, John W.},title = {A Video Representation Using Temporal Superpixels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.

@InProceedings{Zhao_2013_ICCV_Workshops,author = {Zhao, Rui and Ouyang, Wanli and Wang, Xiaogang},title = {Unsupervised Salience Learning for Person Re-identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Structure Preserving Object Tracking

Abstract: Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (based on tracking-by-detection) that resolves this problem by incorporating spatial constraints between the objects. The spatial constraints are learned along with the object detectors using an online structured SVM algorithm. The experimental evaluation of our structure-preserving object tracker (SPOT) reveals significant performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object.

@InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Lu and van der Maaten, Laurens},title = {Structure Preserving Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yicong Tian, Rahul Sukthankar, Mubarak Shah

Abstract: Deformable part models have achieved impressive performance for object detection, even on difficult image datasets. This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video. Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples. For each action model, the most discriminative 3D subvolumes are automatically selected as parts and the spatiotemporal relations between their locations are learned. By focusing on the most distinctive parts of each action, our models adapt to intra-class variation and show robustness to clutter. Extensive experiments on several video datasets demonstrate the strength of spatiotemporal DPMs for classifying and localizing actions.

@InProceedings{Tian_2013_ICCV_Workshops,author = {Tian, Yicong and Sukthankar, Rahul and Shah, Mubarak},title = {Spatiotemporal Deformable Part Models for Action Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Bo Zheng, Yibiao Zhao, Joey C. Yu, Katsushi Ikeuchi, Song-Chun Zhu

Abstract: In this paper, we present an approach for scene understanding by reasoning physical stability of objects from point cloud. We utilize a simple observation that, by human design, objects in static scenes should be stable with respect to gravity. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Our method consists of two major steps: 1) geometric reasoning: recovering solid 3D volumetric primitives from defective point cloud; and 2) physical reasoning: grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior. We propose to use a novel disconnectivity graph (DG) to represent the energy landscape and use a Swendsen-Wang Cut (MCMC) method for optimization. In experiments, we demonstrate that the algorithm achieves substantially better performance for i) object segmentation, ii) 3D volumetric recovery of the scene, and iii) better parsing result for scene understanding in comparison to state-of-the-art methods in both public dataset and our own new dataset.

@InProceedings{Zheng_2013_ICCV_Workshops,author = {Zheng, Bo and Zhao, Yibiao and Yu, Joey C. and Ikeuchi, Katsushi and Zhu, Song-Chun},title = {Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Recovering Stereo Pairs from Anaglyphs

Abstract: An anaglyph is a single image created by selecting complementary colors from a stereo color pair; the user can perceive depth by viewing it through color-filtered glasses. We propose a technique to reconstruct the original color stereo pair given such an anaglyph. We modified SIFT-Flow and use it to initially match the different color channels across the two views. Our technique then iteratively refines the matches, selects the good matches (which defines the "anchor" colors), and propagates the anchor colors. We use a diffusion-based technique for the color propagation, and added a step to suppress unwanted colors. Results on a variety of inputs demonstrate the robustness of our technique. We also extended our method to anaglyph videos by using optic flow between time frames.

@InProceedings{Joulin_2013_ICCV_Workshops,author = {Joulin, Armand and Bing Kang, Sing},title = {Recovering Stereo Pairs from Anaglyphs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Learning a Manifold as an Atlas

Nikolaos Pitelis, Chris Russell, Lourdes Agapito

Abstract: In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining the charts, and the discrete assignment of points to charts. In contrast to existing methods, this direct formulation of a manifold does not require "unwrapping" the manifold into a lower dimensional space and allows us to learn closed manifolds of interest to vision, such as those corresponding to gait cycles or camera pose. We report state-ofthe-art results for manifold based nearest neighbour classification on vision datasets, and show how the same techniques can be applied to the 3D reconstruction of human motion from a single image.

@InProceedings{Pitelis_2013_ICCV_Workshops,author = {Pitelis, Nikolaos and Russell, Chris and Agapito, Lourdes},title = {Learning a Manifold as an Atlas},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Kilho Son, Eduardo B. Almeida, David B. Cooper

Abstract: This paper introduces a novel approach for reassembling pot sherds found at archaeological excavation sites, for the purpose of reconstructing clay pots that had been made on a wheel. These pots and the sherds into which they have broken are axially symmetric. The reassembly process can be viewed as 3D puzzle solving or generalized cylinder learning from broken fragments. The estimation exploits both local and semi-global geometric structure, thus making it a fundamental problem of geometry estimation from noisy fragments in computer vision and pattern recognition. The data used are densely digitized 3D laser scans of each fragment's outer surface. The proposed reassembly system is automatic and functions when the pile of available fragments is from one or multiple pots, and even when pieces are missing from any pot. The geometric structure used are curves on the pot along which the surface had broken and the silhouette of a pot with respect to an axis, called axisprofile curve (APC). For reassembling multiple pots with or without missing pieces, our algorithm estimates the APC from each fragment, then reassembles into configurations the ones having distinctive APC. Further growth of configurations is based on adding remaining fragments such that their APC and break curves are consistent with those of a configuration. The method is novel, more robust and handles the largest numbers of fragments to date.

@InProceedings{Son_2013_ICCV_Workshops,author = {Son, Kilho and Almeida, Eduardo B. and Cooper, David B.},title = {Axially Symmetric 3D Pots Configuration System Using Axis of Symmetry and Break Curve},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid

Abstract: Attributes are an intermediate representation, which enables parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e.g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.

@InProceedings{Akata_2013_ICCV_Workshops,author = {Akata, Zeynep and Perronnin, Florent and Harchaoui, Zaid and Schmid, Cordelia},title = {Label-Embedding for Attribute-Based Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Christian Theriault, Nicolas Thome, Matthieu Cord

Abstract: In this paper, we address the challenging problem of categorizing video sequences composed of dynamic natural scenes. Contrarily to previous methods that rely on handcrafted descriptors, we propose here to represent videos using unsupervised learning of motion features. Our method encompasses three main contributions: 1) Based on the Slow Feature Analysis principle, we introduce a learned local motion descriptor which represents the principal and more stable motion components of training videos. 2) We integrate our local motion feature into a global coding/pooling architecture in order to provide an effective signature for each video sequence. 3) We report state of the art classification performances on two challenging natural scenes data sets. In particular, an outstanding improvement of 11% in classification score is reached on a data set introduced in 2012.

@InProceedings{Theriault_2013_ICCV_Workshops,author = {Theriault, Christian and Thome, Nicolas and Cord, Matthieu},title = {Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Petr Gronat, Guillaume Obozinski, Josef Sivic, Tomas Pajdla

Abstract: The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database. This is a challenging task due to changes in viewpoint, imaging conditions and the large size of the image database. The contribution of this work is two-fold. First, we cast the place recognition problem as a classification task and use the available geotags to train a classifier for each location in the database in a similar manner to per-exemplar SVMs in object recognition. Second, as only few positive training examples are available for each location, we propose a new approach to calibrate all the per-location SVM classifiers using only the negative examples. The calibration we propose relies on a significance measure essentially equivalent to the p-values classically used in statistical hypothesis testing. Experiments are performed on a database of 25,000 geotagged street view images of Pittsburgh and demonstrate improved place recognition accuracy of the proposed approach over the previous work.

@InProceedings{Gronat_2013_ICCV_Workshops,author = {Gronat, Petr and Obozinski, Guillaume and Sivic, Josef and Pajdla, Tomas},title = {Learning and Calibrating Per-Location Classifiers for Visual Place Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Austin Abrams, Kylia Miskell, Robert Pless

Abstract: Shadows encode a powerful geometric cue: if one pixel casts a shadow onto another, then the two pixels are colinear with the lighting direction. Given many images over many lighting directions, this constraint can be leveraged to recover the depth of a scene from a single viewpoint. For outdoor scenes with solar illumination, we term this the episolar constraint, which provides a convex optimization to solve for the sparse depth of a scene from shadow correspondences, a method to reduce the search space when finding shadow correspondences, and a method to geometrically calibrate a camera using shadow constraints. Our method constructs a dense network of nonlocal constraints which complements recent work on outdoor photometric stereo and cloud based cues for 3D. We demonstrate results across a variety of time-lapse sequences from webcams "in the wild."

@InProceedings{Abrams_2013_ICCV_Workshops,author = {Abrams, Austin and Miskell, Kylia and Pless, Robert},title = {The Episolar Constraint: Monocular Shape from Shadow Correspondence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Margret Keuper, Thorsten Schmidt, Maja Temerinac-Ott, Jan Padeken, Patrick Heun, Olaf Ronneberger, Thomas Brox

Abstract: With volumetric data from widefield fluorescence microscopy, many emerging questions in biological and biomedical research are being investigated. Data can be recorded with high temporal resolution while the specimen is only exposed to a low amount of phototoxicity. These advantages come at the cost of strong recording blur caused by the infinitely extended point spread function (PSF). For widefield microscopy, its magnitude only decays with the square of the distance to the focal point and consists of an airy bessel pattern which is intricate to describe in the spatial domain. However, the Fourier transform of the incoherent PSF (denoted as Optical Transfer Function (OTF)) is well localized and smooth. In this paper, we present a blind deconvolution method that improves results of state-of-theart deconvolution methods on widefield data by exploiting the properties of the widefield OTF.

@InProceedings{Keuper_2013_ICCV_Workshops,author = {Keuper, Margret and Schmidt, Thorsten and Temerinac-Ott, Maja and Padeken, Jan and Heun, Patrick and Ronneberger, Olaf and Brox, Thomas},title = {Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF)},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Tensor-Based Human Body Modeling

Yinpeng Chen, Zicheng Liu, Zhengyou Zhang

Abstract: In this paper, we present a novel approach to model 3D human body with variations on both human shape and pose, by exploring a tensor decomposition technique. 3D human body modeling is important for 3D reconstruction and animation of realistic human body, which can be widely used in Tele-presence and video game applications. It is challenging due to a wide range of shape variations over different people and poses. The existing SCAPE model [4] is popular in computer vision for modeling 3D human body. However, it considers shape and pose deformations separately, which is not accurate since pose deformation is persondependent. Our tensor-based model addresses this issue by jointly modeling shape and pose deformations. Experimental results demonstrate that our tensor-based model outperforms the SCAPE model quite significantly. We also apply our model to capture human body using Microsoft Kinect sensors with excellent results.

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Yinpeng and Liu, Zicheng and Zhang, Zhengyou},title = {Tensor-Based Human Body Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Xing Mei, Xun Sun, Weiming Dong, Haitao Wang, Xiaopeng Zhang

Abstract: This paper presents a novel tree-based cost aggregation method for dense stereo matching. Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation. Conceptually, the segment-tree is constructed in a three-step process: first, the pixels are grouped into a set of segments with the reference color or intensity image; second, a tree graph is created for each segment; and in the final step, these independent segment graphs are linked to form the segment-tree structure. In practice, this tree can be efficiently built in time nearly linear to the number of the image pixels. Compared to MST where the graph connectivity is determined with local edge weights, our method introduces some 'non-local' decision rules: the pixels in one perceptually consistent segment are more likely to share similar disparities, and therefore their connectivity within the segment should be first enforced in the tree construction process. The matching costs are then aggregated over the tree within two passes. Performance evaluation on 19 Middlebury data sets shows that the proposed method is comparable to previous state-of-the-art aggregation methods in disparity accuracy and processing speed. Furthermore, the tree structure can be refined with the estimated disparities, which leads to consistent scene segmentation and significantly better aggregation results.

@InProceedings{Mei_2013_ICCV_Workshops,author = {Mei, Xing and Sun, Xun and Dong, Weiming and Wang, Haitao and Zhang, Xiaopeng},title = {Segment-Tree Based Cost Aggregation for Stereo Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Lubor Ladicky, Philip H.S. Torr, Andrew Zisserman

Abstract: Our goal is to detect humans and estimate their 2D pose in single images. In particular, handling cases of partial visibility where some limbs may be occluded or one person is partially occluding another. Two standard, but disparate, approaches have developed in the field: the first is the part based approach for layout type problems, involving optimising an articulated pictorial structure; the second is the pixel based approach for image labelling involving optimising a random field graph defined on the image. Our novel contribution is a formulation for pose estimation which combines these two models in a principled way in one optimisation problem and thereby inherits the advantages of both of them. Inference on this joint model finds the set of instances of persons in an image, the location of their joints, and a pixel-wise body part labelling. We achieve near or state of the art results on standard human pose data sets, and demonstrate the correct estimation for cases of self-occlusion, person overlap and image truncation.

@InProceedings{Ladicky_2013_ICCV_Workshops,author = {Ladicky, Lubor and Torr, Philip H.S. and Zisserman, Andrew},title = {Human Pose Estimation Using a Joint Pixel-wise and Part-wise Formulation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao

Abstract: An object model base that covers a large number of object categories is of great value for many computer vision tasks. As artifacts are usually designed to have various textures, their structure is the primary distinguishing feature between different categories. Thus, how to encode this structural information and how to start the model learning with a minimum of human labeling become two key challenges for the construction of the model base. We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes. However, the incremental model learning may be biased due to the limited human labeling. Therefore, we propose a new strategy that uses the depth information in RGBD images to guide the model learning for object detection in ordinary RGB images. In experiments, the proposed method achieves superior performance as good as the supervised methods that require the labeling of all target objects.

@InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Quanshi and Song, Xuan and Shao, Xiaowei and Shibasaki, Ryosuke and Zhao, Huijing},title = {Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Learning Separable Filters

Roberto Rigamonti, Amos Sironi, Vincent Lepetit, Pascal Fua

Abstract: Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, making their use computationally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-theart methods on the linear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is general and can be used on generic filter banks to reduce the complexity of the convolutions.

@InProceedings{Rigamonti_2013_ICCV_Workshops,author = {Rigamonti, Roberto and Sironi, Amos and Lepetit, Vincent and Fua, Pascal},title = {Learning Separable Filters},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Varun Ramakrishna, Takeo Kanade, Yaser Sheikh

Abstract: The human body is structurally symmetric. Tracking by detection approaches for human pose suffer from double counting, where the same image evidence is used to explain two separate but symmetric parts, such as the left and right feet. Double counting, if left unaddressed can critically affect subsequent processes, such as action recognition, affordance estimation, and pose reconstruction. In this work, we present an occlusion aware algorithm for tracking human pose in an image sequence, that addresses the problem of double counting. Our key insight is that tracking human pose can be cast as a multi-target tracking problem where the "targets" are related by an underlying articulated structure. The human body is modeled as a combination of singleton parts (such as the head and neck) and symmetric pairs of parts (such as the shoulders, knees, and feet). Symmetric body parts are jointly tracked with mutual exclusion constraints to prevent double counting by reasoning about occlusion. We evaluate our algorithm on an outdoor dataset with natural background clutter, a standard indoor dataset (HumanEva-I), and compare against a state of the art pose estimation algorithm.

@InProceedings{Ramakrishna_2013_ICCV_Workshops,author = {Ramakrishna, Varun and Kanade, Takeo and Sheikh, Yaser},title = {Tracking Human Pose by Tracking Symmetric Parts},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Facial feature tracking is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since faces may have varying facial expressions, poses or occlusions. In this paper, we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark databases show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.

@InProceedings{Wu_2013_ICCV_Workshops,author = {Wu, Yue and Wang, Zuoguan and Ji, Qiang},title = {Facial Feature Tracking Under Varying Facial Expressions and Face Poses Based on Restricted Boltzmann Machines},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Roni Mittelman, Honglak Lee, Benjamin Kuipers, Silvio Savarese

Abstract: The use of semantic attributes in computer vision problems has been gaining increased popularity in recent years. Attributes provide an intermediate feature representation in between low-level features and the class categories, leading to improved learning on novel categories from few examples. However, a major caveat is that learning semantic attributes is a laborious task, requiring a significant amount of time and human intervention to provide labels. In order to address this issue, we propose a weakly supervised approach to learn mid-level features, where only class-level supervision is provided during training. We develop a novel extension of the restricted Boltzmann machine (RBM) by incorporating a Beta-Bernoulli process factor potential for hidden units. Unlike the standard RBM, our model uses the class labels to promote category-dependent sharing of learned features, which tends to improve the generalization performance. By using semantic attributes for which annotations are available, we show that we can find correspondences between the learned mid-level features and the labeled attributes. Therefore, the mid-level features have distinct semantic characterization which is similar to that given by the semantic attributes, even though their labeling was not provided during training. Our experimental results on object recognition tasks show significant performance gains, outperforming existing methods which rely on manually labeled semantic attributes.

@InProceedings{Mittelman_2013_ICCV_Workshops,author = {Mittelman, Roni and Lee, Honglak and Kuipers, Benjamin and Savarese, Silvio},title = {Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Rui Caseiro, Pedro Martins, Joao F. Henriques, Fatima Silva Leite, Jorge Batista

Abstract: In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27]. A popular framework, valid over any Riemannian manifold, was proposed in [31] for binary classification. Once moving from binary to multi-class classification this paradigm is not valid anymore, due to the spread of multiple positive classes on the manifold [27]. It is then natural to ask whether the multi-class paradigm could be extended to operate on a large class of Riemannian manifolds. We propose a mathematically well-founded classification paradigm that allows to extend the work in [31] to multi-class models, taking into account the structure of the space. The idea is to project all the data from the manifold onto an affine tangent space at a particular point. To mitigate the distortion induced by local diffeomorphisms, we introduce for the first time in the computer vision community a well-founded mathematical concept, so-called Rolling map [21, 16]. The novelty in this alternate school of thought is that the manifold will be firstly rolled (without slipping or twisting) as a rigid body, then the given data is unwrapped onto the affine tangent space, where the classification is performed.

@InProceedings{Caseiro_2013_ICCV_Workshops,author = {Caseiro, Rui and Martins, Pedro and Henriques, Joao F. and Silva Leite, Fatima and Batista, Jorge},title = {Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In computer vision there has been increasing interest in learning hashing codes whose Hamming distance approximates the data similarity. The hashing functions play roles in both quantizing the vector space and generating similarity-preserving codes. Most existing hashing methods use hyper-planes (or kernelized hyper-planes) to quantize and encode. In this paper, we present a hashing method adopting the k-means quantization. We propose a novel Affinity-Preserving K-means algorithm which simultaneously performs k-means clustering and learns the binary indices of the quantized cells. The distance between the cells is approximated by the Hamming distance of the cell indices. We further generalize our algorithm to a product space for learning longer codes. Experiments show our method, named as K-means Hashing (KMH), outperforms various state-of-the-art hashing encoding methods.

@InProceedings{He_2013_ICCV_Workshops,author = {He, Kaiming and Wen, Fang and Sun, Jian},title = {K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Julien P.C. Valentin, Sunando Sengupta, Jonathan Warrell, Ali Shahrokni, Philip H.S. Torr

Abstract: Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset ( RGB D images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTI odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies.

@InProceedings{Valentin_2013_ICCV_Workshops,author = {Valentin, Julien P.C. and Sengupta, Sunando and Warrell, Jonathan and Shahrokni, Ali and Torr, Philip H.S.},title = {Mesh Based Semantic Modelling for Indoor and Outdoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Go Irie, Dong Liu, Zhenguo Li, Shih-Fu Chang

Abstract: nary learning methods rely on image descriptors alone or together with class labels. However, Web images are often associated with text data which may carry substantial information regarding image semantics, and may be exploited for visual dictionary learning. This paper explores this idea by leveraging relational information between image descriptors and textual words via co-clustering, in addition to information of image descriptors. Existing co-clustering methods are not optimal for this problem because they ignore the structure of image descriptors in the continuous space, which is crucial for capturing visual characteristics of images. We propose a novel Bayesian co-clustering model to jointly estimate the underlying distributions of the continuous image descriptors as well as the relationship between such distributions and the textual words through a unified Bayesian inference. Extensive experiments on image categorization and retrieval have validated the substantial value of the proposed joint modeling in improving visual dictionary learning, where our model shows superior performance over several recent methods.

@InProceedings{Irie_2013_ICCV_Workshops,author = {Irie, Go and Liu, Dong and Li, Zhenguo and Chang, Shih-Fu},title = {A Bayesian Approach to Multimodal Visual Dictionary Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Photometric Ambient Occlusion

Daniel Hauagge, Scott Wehrwein, Kavita Bala, Noah Snavely

Abstract: We present a method for computing ambient occlusion (AO) for a stack of images of a scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision, we show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model. We use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark. We also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry.

@InProceedings{Hauagge_2013_ICCV_Workshops,author = {Hauagge, Daniel and Wehrwein, Scott and Bala, Kavita and Snavely, Noah},title = {Photometric Ambient Occlusion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Jiangbo Lu, Hongsheng Yang, Dongbo Min, Minh N. Do

Abstract: Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge, which is often the case for (subpixelaccurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the PatchMatch method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. This paper presents a generic and fast computational framework for general multi-labeling problems called PatchMatch Filter (PMF). For the very first time, we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., PatchMatch-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact superpixels, we also propose superpixelbased novel search strategies that generalize and improve the original PatchMatch method. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve state-of-the-art correspondence accuracy but run much faster than other competing methods, often giving over 10-times speedup for large label space cases.

@InProceedings{Lu_2013_ICCV_Workshops,author = {Lu, Jiangbo and Yang, Hongsheng and Min, Dongbo and Do, Minh N.},title = {Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.

@InProceedings{Wang_2013_ICCV_Workshops,author = {Wang, Fang and Li, Yi},title = {Beyond Physical Connections: Tree Models in Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Sumit Shekhar, Vishal M. Patel, Hien V. Nguyen, Rama Chellappa

Abstract: Data-driven dictionaries have produced state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. Specifically, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. An efficient optimization technique is presented, which can be easily kernelized and extended to multiple domains. The algorithm is modified to learn a common discriminative dictionary, which can be further used for classification. The proposed approach does not require any explicit correspondence between the source and target domains, and shows good results even when there are only a few labels available in the target domain. Various recognition experiments show that the method performs on par or better than competitive stateof-the-art methods.

@InProceedings{Shekhar_2013_ICCV_Workshops,author = {Shekhar, Sumit and Patel, Vishal M. and Nguyen, Hien V. and Chellappa, Rama},title = {Generalized Domain-Adaptive Dictionaries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2 nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2 nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-ofthe-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.

@InProceedings{Xiong_2013_ICCV_Workshops,author = {Xiong, Xuehan and De la Torre, Fernando},title = {Supervised Descent Method and Its Applications to Face Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Scholkopf

Abstract: Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-ofthe-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur.

@InProceedings{Schuler_2013_ICCV_Workshops,author = {Schuler, Christian J. and Christopher Burger, Harold and Harmeling, Stefan and Scholkopf, Bernhard},title = {A Machine Learning Approach for Non-blind Image Deconvolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We address the problem of long-term object tracking, where the object may become occluded or leave-the-view. In this setting, we show that an accurate appearance model is considerably more effective than a strong motion model. We develop simple but effective algorithms that alternate between tracking and learning a good appearance model given a track. We show that it is crucial to learn from the "right" frames, and use the formalism of self-paced curriculum learning to automatically select such frames. We leverage techniques from object detection for learning accurate appearance-based templates, demonstrating the importance of using a large negative training set (typically not used for tracking). We describe both an offline algorithm (that processes frames in batch) and a linear-time online (i.e. causal) algorithm that approaches real-time performance. Our models significantly outperform prior art, reducing the average error on benchmark videos by a factor of 4.

@InProceedings{Supancic_2013_ICCV_Workshops,author = {Supancic, III, James S. and Ramanan, Deva},title = {Self-Paced Learning for Long-Term Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Vishnu Naresh Boddeti, Takeo Kanade, B.V.K. Vijaya Kumar

Abstract: Alignment of 3D objects from 2D images is one of the most important and well studied problems in computer vision. A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors. Since errors in landmark initialization from the appearance model propagate through the shape model, it is critical to have a robust landmark appearance model. While there has been much progress in designing sophisticated and robust shape models, there has been relatively less progress in designing robust landmark detection models. In this paper we present an efficient and robust landmark detection model which is designed specifically to minimize localization errors thereby leading to state-of-the-art object alignment performance. We demonstrate the efficacy and speed of the proposed approach on the challenging task of multi-view car alignment.

@InProceedings{Boddeti_2013_ICCV_Workshops,author = {Naresh Boddeti, Vishnu and Kanade, Takeo and Vijaya Kumar, B.V.K.},title = {Correlation Filters for Object Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Jeremie Papon, Alexey Abramov, Markus Schoeler, Florentin Worgotter

Abstract: Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpixels, is a widely used preprocessing step in segmentation algorithms. Superpixel methods reduce the number of regions that must be considered later by more computationally expensive algorithms, with a minimal loss of information. Nevertheless, as some information is inevitably lost, it is vital that superpixels not cross object boundaries, as such errors will propagate through later steps. Existing methods make use of projected color or depth information, but do not consider three dimensional geometric relationships between observed data points which can be used to prevent superpixels from crossing regions of empty space. We propose a novel over-segmentation algorithm which uses voxel relationships to produce over-segmentations which are fully consistent with the spatial geometry of the scene in three dimensional, rather than projective, space. Enforcing the constraint that segmented regions must have spatial connectivity prevents label flow across semantic object boundaries which might otherwise be violated. Additionally, as the algorithm works directly in 3D space, observations from several calibrated RGB+D cameras can be segmented jointly. Experiments on a large data set of human annotated RGB+D images demonstrate a significant reduction in occurrence of clusters crossing object boundaries, while maintaining speeds comparable to state-of-the-art 2D methods.

@InProceedings{Papon_2013_ICCV_Workshops,author = {Papon, Jeremie and Abramov, Alexey and Schoeler, Markus and Worgotter, Florentin},title = {Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Shaodi You, Robby T. Tan, Rei Kawakami, Katsushi Ikeuchi

Abstract: Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatiotemporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.

@InProceedings{You_2013_ICCV_Workshops,author = {You, Shaodi and Tan, Robby T. and Kawakami, Rei and Ikeuchi, Katsushi},title = {Adherent Raindrop Detection and Removal in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Dengfeng Chai, Wolfgang Forstner, Florent Lafarge

Abstract: The automatic extraction of line-networks from images is a well-known computer vision issue. Appearance and shape considerations have been deeply explored in the literature to improve accuracy in presence of occlusions, shadows, and a wide variety of irrelevant objects. However most existing works have ignored the structural aspect of the problem. We present an original method which provides structurally-coherent solutions. Contrary to the pixelbased and object-based methods, our result is a graph in which each node represents either a connection or an ending in the line-network. Based on stochastic geometry, we develop a new family of point processes consisting in sampling junction-points in the input image by using a Monte Carlo mechanism. The quality of a configuration is measured by a probability density which takes into account both image consistency and shape priors. Our experiments on a variety of problems illustrate the potential of our approach in terms of accuracy, flexibility and efficiency.

@InProceedings{Chai_2013_ICCV_Workshops,author = {Chai, Dengfeng and Forstner, Wolfgang and Lafarge, Florent},title = {Recovering Line-Networks in Images by Junction-Point Processes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, where the global problem is split into subproblems defined over the graph cliques. The optimal solution to these subproblems is obtained by making use of a polynomial system solver. Our algorithm inherits the convergence guarantees of dual decomposition. To speed up optimization, we also introduce a variant of this algorithm based on the augmented Lagrangian method. Our experiments illustrate the diversity of computer vision problems that can be expressed with polynomial energies, and demonstrate the benefits of our approach over existing continuous inference methods.

@InProceedings{Salzmann_2013_ICCV_Workshops,author = {Salzmann, Mathieu},title = {Continuous Inference in Graphical Models with Polynomial Energies},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class; it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.

@InProceedings{Wah_2013_ICCV_Workshops,author = {Wah, Catherine and Belongie, Serge},title = {Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person reidentification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual features of an image pair from different views are first locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsityinducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach.

@InProceedings{Li_2013_ICCV_Workshops,author = {Li, Wei and Wang, Xiaogang},title = {Locally Aligned Feature Transforms across Views},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}