Jian Cheng, Cong Leng, Jiaxiang Wu, Hainan Cui, Hanqing Lu

Abstract: Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. Therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.

@InProceedings{Cheng_2014_CVPR,author = {Cheng, Jian and Leng, Cong and Wu, Jiaxiang and Cui, Hainan and Lu, Hanqing},title = {Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Predicting Matchability

Wilfried Hartmann, Michal Havlena, Konrad Schindler

Abstract: The initial steps of many computer vision algorithms are interest point extraction and matching. In larger image sets the pairwise matching of interest point descriptors between images is an important bottleneck. For each descriptor in one image the (approximate) nearest neighbor in the other one has to be found and checked against the second-nearest neighbor to ensure the correspondence is unambiguous. Here, we asked the question how to best decimate the list of interest points without losing matches, i.e. we aim to speed up matching by filtering out, in advance, those points which would not survive the matching stage. It turns out that the best filtering criterion is not the response of the interest point detector, which in fact is not surprising: the goal of detection are repeatable and well-localized points, whereas the objective of the selection are points whose descriptors can be matched successfully. We show that one can in fact learn to predict which descriptors are matchable, and thus reduce the number of interest points significantly without losing too many matches. We show that this strategy, as simple as it is, greatly improves the matching success with the same number of points per image. Moreover, we embed the prediction in a state-of-the-art Structure-from-Motion pipeline and demonstrate that it also outperforms other selection methods at system level.

@InProceedings{Hartmann_2014_CVPR,author = {Hartmann, Wilfried and Havlena, Michal and Schindler, Konrad},title = {Predicting Matchability},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Trinocular Geometry Revisited

Abstract: When do the visual rays associated with triplets of point correspondences converge, that is, intersect in a common point? Classical models of trinocular geometry based on the fundamental matrices and trifocal tensor associated with the corresponding cameras only provide partial answers to this fundamental question, in large part because of underlying, but seldom explicit, general configuration assumptions. This paper uses elementary tools from projective line geometry to provide necessary and sufficient geometric and analytical conditions for convergence in terms of transversals to triplets of visual rays, without any such assumptions. In turn, this yields a novel and simple minimal parameterization of trinocular geometry for cameras with non-collinear or collinear pinholes.

@InProceedings{Ponce_2014_CVPR,author = {Ponce, Jean and Hebert, Martial},title = {Trinocular Geometry Revisited},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yubin Kuang, Jan E. Solem, Fredrik Kahl, Kalle Astrom

Abstract: In this paper, we study the problems of estimating relative pose between two cameras in the presence of radial distortion.  Specifically, we consider minimal problems where one of the cameras has no or known radial distortion.  There are three useful cases for this setup with a single unknown distortion: (i) fundamental matrix estimation where the two cameras are uncalibrated, (ii) essential matrix estimation for a partially calibrated camera pair, (iii) essential matrix estimation for one calibrated camera and one camera with unknown focal length. We study the parameterization of these three problems and derive fast polynomial solvers based on Grobner basis methods.  We demonstrate the numerical stability of the solvers on synthetic data.  The minimal solvers have also been applied to real imagery with convincing results

@InProceedings{Kuang_2014_CVPR,author = {Kuang, Yubin and Solem, Jan E. and Kahl, Fredrik and Astrom, Kalle},title = {Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we study the configurations of motion and structure that lead to inherent ambiguities in radial distortion estimation  (or 3D reconstruction with unknown radial distortions). By analyzing the motion field of radially distorted images, we solve for critical surface pairs that can lead to the same motion field under different radial distortions and possibly different camera motions. We study the properties of the discovered critical configurations and discuss the practically important configurations that often occur in real applications. We demonstrate the impact of the radial distortion ambiguity on multi-view reconstruction with synthetic experiments and real experiments.

@InProceedings{Wu_2014_CVPR,author = {Wu, Changchang},title = {Critical Configurations For Radial Distortion Self-Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Fabio Galasso, Margret Keuper, Thomas Brox, Bernt Schiele

Abstract: Computational and memory costs restrict spectral techniques to rather small graphs, which is a serious limitation especially in video segmentation. In this paper, we propose the use of a reduced graph based on superpixels. In contrast to previous work, the reduced graph is reweighted such that the resulting segmentation is equivalent, under certain assumptions, to that of the full graph. We consider equivalence in terms of the normalized cut and of its spectral clustering relaxation. The proposed method reduces runtime and memory consumption and yields on par results in image and video segmentation. Further, it enables an efficient data representation and update for a new streaming video segmentation approach that also achieves state-of-the-art performance.

@InProceedings{Galasso_2014_CVPR,author = {Galasso, Fabio and Keuper, Margret and Brox, Thomas and Schiele, Bernt},title = {Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Reconstructing PASCAL VOC

Sara Vicente, Joao Carreira, Lourdes Agapito, Jorge Batista

Abstract: We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion, then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets, PASCAL VOC. Our results may re-stimulate once popular geometry-oriented model-based recognition approaches.

@InProceedings{Vicente_2014_CVPR,author = {Vicente, Sara and Carreira, Joao and Agapito, Lourdes and Batista, Jorge},title = {Reconstructing PASCAL VOC},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Thorsten Beier, Thorben Kroeger, Jorg H. Kappes, Ullrich Kothe, Fred A. Hamprecht

Abstract: Recently, unsupervised image segmentation has become increasingly popular. Starting from a superpixel segmentation, an edge-weighted region adjacency graph is constructed. Amongst all segmentations of the graph, the one which best conforms to the given image evidence, as measured by the sum of cut edge weights, is chosen.  Since this problem is NP-hard, we propose a new approximate solver based on the move-making paradigm: first, the graph is recursively partitioned into small regions (cut phase). Then, for any two adjacent regions, we consider alternative cuts of these two regions defining possible moves (glue & cut phase). For planar problems, the optimal move can be found, whereas for non-planar problems, efficient approximations exist.  We evaluate our algorithm on published and new benchmark datasets, which we make available here. The proposed algorithm finds segmentations that, as measured by a loss function, are as close to the ground-truth as the global optimum found by exact solvers. It does so significantly faster then existing approximate methods, which is important for large-scale problems.

@InProceedings{Beier_2014_CVPR,author = {Beier, Thorsten and Kroeger, Thorben and Kappes, Jorg H. and Kothe, Ullrich and Hamprecht, Fred A.},title = {Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xiao Liu, Dacheng Tao, Mingli Song, Ying Ruan, Chun Chen, Jiajun Bu

Abstract: The desire of enabling computers to learn semantic concepts from large quantities of Internet videos has motivated increasing interests on semantic video understanding, while video segmentation is important yet challenging for understanding videos. The main difficulty of video segmentation arises from the burden of labeling training samples, making the problem largely unsolved. In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation. Whereas previous weakly supervised video segmentation methods have been limited to the two-class case, our proposed scheme focuses on more challenging multiclass video segmentation, which finds a semantically meaningful label for every pixel in a video. Our scheme enjoys several favorable properties when compared with conventional methods. First, a weakly supervised hashing procedure is carried out to handle both metric and semantic similarity. Second, the proposed nearest neighbor-based label transfer algorithm effectively avoids overfitting caused by weakly supervised data. Third, a multi-video graph model is built to encourage smoothness between regions that are spatiotemporally adjacent and similar in appearance. We demonstrate the effectiveness of the proposed scheme by comparing it with several other state-of-the-art weakly supervised segmentation methods on one new Wild8 dataset and two other publicly available datasets.

@InProceedings{Liu_2014_CVPR,author = {Liu, Xiao and Tao, Dacheng and Song, Mingli and Ruan, Ying and Chen, Chun and Bu, Jiajun},title = {Weakly Supervised Multiclass Video Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Video motion segmentation techniques automatically segment and track objects and regions from videos or image sequences as a primary processing step for many computer vision applications. We propose a novel motion segmentation approach for both rigid and non-rigid objects using adaptive manifold denoising.  We first introduce an adaptive kernel space in which two feature trajectories are mapped into the same point if they belong to the same rigid object. After that, we employ an embedded manifold denoising approach with the adaptive kernel to segment the motion of rigid and non-rigid objects. The major observation is that the non-rigid objects often lie on a smooth manifold with deviations which can be removed by manifold denoising. We also show that performing manifold denoising on the kernel space is equivalent to doing so on its range space, which theoretically justifies the embedded manifold denoising on the adaptive kernel space. Experimental results indicate that our algorithm, named Adaptive Manifold Denoising (AMD), is suitable for both rigid and non-rigid motion segmentation. Our algorithm works well in many cases where several state-of-the-art algorithms fail.

@InProceedings{Luo_2014_CVPR,author = {Luo, Dijun and Huang, Heng},title = {Video Motion Segmentation Using New Adaptive Manifold Denoising Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Pulling Things out of Perspective

Lubor Ladicky, Jianbo Shi, Marc Pollefeys

Abstract: The limitations of current state-of-the-art methods for single-view depth estimation and semantic segmentations are closely tied to the property of perspective geometry, that the perceived size of the objects scales inversely with the distance.  In this paper, we show that we can use this property to reduce the learning of a pixel-wise depth classifier to a much simpler classifier predicting only the likelihood of a pixel being at an arbitrarily fixed canonical depth. The likelihoods for any other depths can be obtained by applying the same classifier after appropriate image manipulations. Such transformation of the problem to the canonical depth removes the training data bias towards certain depths and the effect of perspective. The approach can be straight-forwardly generalized to multiple semantic classes, improving both depth estimation and semantic segmentation performance by directly targeting the weaknesses of independent approaches. Conditioning the semantic label on the depth provides a way to align the data to their physical scale, allowing to learn a more discriminative classifier. Conditioning depth on the semantic class helps the classifier to distinguish between ambiguities of the otherwise ill-posed problem.  We tested our algorithm on the KITTI road scene dataset and NYU2 indoor dataset and obtained obtained results that significantly outperform current state-of-the-art in both single-view depth and semantic segmentation domain.

@InProceedings{Ladicky_2014_CVPR,author = {Ladicky, Lubor and Shi, Jianbo and Pollefeys, Marc},title = {Pulling Things out of Perspective},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier – An idea that is intuitively supported by the hierarchical nature of the decision forest model where the input space is typically left unchanged during training and testing. We bridge this gap by introducing randomized Multi- Layer Perceptrons (rMLP) as new split nodes which are capable of learning non-linear, data-specific representations and taking advantage of them by finding optimal predictions for the emerging child nodes. To prevent overfitting, we i) randomly select the image data fed to the input layer, ii) automatically adapt the rMLP topology to meet the complexity of the data arriving at the node and iii) introduce an l1-norm based regularization that additionally sparsifies the network. The key findings in our experiments on three different semantic image labelling datasets are consistently improved results and significantly compressed trees compared to conventional classification trees.

@InProceedings{Bulo_2014_CVPR,author = {Rota Bulo, Samuel and Kontschieder, Peter},title = {Neural Decision Forests for Semantic Image Labelling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Stephan Liwicki, Minh-Tri Pham, Stefanos Zafeiriou, Maja Pantic, Bjorn Stenger

Abstract: In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale and translation which outperforms the previous state-of-the-art approach on a public dataset. For the latter, we incorporate online subspace learning with the proposed FAQ representation to highlight the benefits of the new representation.

@InProceedings{Liwicki_2014_CVPR,author = {Liwicki, Stephan and Pham, Minh-Tri and Zafeiriou, Stefanos and Pantic, Maja and Stenger, Bjorn},title = {Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma, Alexander G. Hauptmann

Abstract: We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance.

@InProceedings{Xu_2014_CVPR,author = {Xu, Zhongwen and Tsang, Ivor W. and Yang, Yi and Ma, Zhigang and Hauptmann, Alexander G.},title = {Event Detection using Multi-Level Relevance Labels and Multiple Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.

@InProceedings{Borji_2014_CVPR,author = {Borji, Ali and Itti, Laurent},title = {Human vs. Computer in Scene and Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where  labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm.  In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.

@InProceedings{Mahmood_2014_CVPR,author = {Mahmood, Arif and Mian, Ajmal and Owens, Robyn},title = {Semi-supervised Spectral Clustering for Image Set Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptation of Global Haar (GHaar) classifiers for vehicle detection under challenging lighting conditions. The system defines the driver's direction of attention (in 6 degrees of freedom), yawning and head-nodding detection, as well as vehicle detection, and distance estimation. Having both road and driver's behaviour information, and implementing a fuzzy fusion system, we develop an integrated framework to cover all of the above subjects. We provide real-time performance analysis for real-world driving scenarios.

@InProceedings{Rezaei_2014_CVPR,author = {Rezaei, Mahdi and Klette, Reinhard},title = {Look at the Driver, Look at the Road: No Distraction! No Accident!},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.

@InProceedings{Zhao_2014_CVPR,author = {Zhao, Rui and Ouyang, Wanli and Wang, Xiaogang},title = {Learning Mid-level Filters for Person Re-identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andrew Gardner, Jinko Kanno, Christian A. Duncan, Rastko Selmic

Abstract: We present a distance metric based upon the notion of minimum-cost injective mappings between sets. Our function satisfies metric properties as long as the cost of the minimum mappings is derived from a semimetric, for which the triangle inequality is not necessarily satisfied. We show that the Jaccard distance (alternatively biotope, Tanimoto, or Marczewski-Steinhaus distance) may be considered the special case for finite sets where costs are derived from the discrete metric. Extensions that allow premetrics (not necessarily symmetric), multisets (generalized to include probability distributions), and asymmetric mappings are given that expand the versatility of the metric without sacrificing metric properties. The function has potential applications in pattern recognition, machine learning, and information retrieval.

@InProceedings{Gardner_2014_CVPR,author = {Gardner, Andrew and Kanno, Jinko and Duncan, Christian A. and Selmic, Rastko},title = {Measuring Distance Between Unordered Sets of Different Sizes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Wei Li, Rui Zhao, Tong Xiao, Xiaogang Wang

Abstract: Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection.  In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. The learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.

@InProceedings{Li_2014_CVPR,author = {Li, Wei and Zhao, Rui and Xiao, Tong and Wang, Xiaogang},title = {DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes. The texture feature is constructed by concatenating the lacunarity-related parameters estimated from the multi-scale local binary patterns of image. Benefiting from the ability of lacunarity analysis to distinguish spatial patterns, our method is able to characterize the spatial distribution of local image structures from multiple scales. The proposed feature was applied to texture classification and has demonstrated excellent performance in comparison with several state-of-the-art approaches on four benchmark datasets.

@InProceedings{Quan_2014_CVPR,author = {Quan, Yuhui and Xu, Yong and Sun, Yuping and Luo, Yu},title = {Lacunarity Analysis on Image Patterns for Texture Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Eduard Trulls, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer

Abstract: In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).  The merit of our approach lies in "cleaning up" the low-level HOG features by exploiting the spatial support of SLIC superpixels; this can be understood as using segmentation to split the feature variation into object-specific  and background changes. Rather than committing to a single segmentation we use a large pool of SLIC superpixels and combine them in a scale-, position- and object-dependent manner to build soft segmentation masks. The segmentation masks can be computed fast enough to repeat this process over every candidate window, during training and detection, for both the root and part filters of DPMs.  We use these masks to construct enhanced, background-invariant features to train DPMs. We test our approach on the PASCAL VOC 2007, outperforming the standard DPM in 17 out of 20 classes, yielding an average increase of 1.7% AP. Additionally, we demonstrate the robustness of this approach, extending it to dense SIFT descriptors for large displacement optical flow.

@InProceedings{Trulls_2014_CVPR,author = {Trulls, Eduard and Tsogkas, Stavros and Kokkinos, Iasonas and Sanfeliu, Alberto and Moreno-Noguer, Francesc},title = {Segmentation-aware Deformable Part Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking.  Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets.

@InProceedings{Hall_2014_CVPR,author = {Hall, David and Perona, Pietro},title = {From Categories to Individuals in Real Time -- A Unified Boosting Approach},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mahdi M. Kalayeh, Haroon Idrees, Mubarak Shah

Abstract: The real world image databases such as Flickr are characterized by continuous addition of new images. The recent approaches for image annotation, i.e. the problem of assigning tags to images, have two major drawbacks. First, either models are learned using the entire training data, or to handle the issue of dataset imbalance, tag-specific discriminative models are trained. Such models become obsolete and require relearning when new images and tags are added to database. Second, the task of feature-fusion is typically dealt using ad-hoc approaches. In this paper, we present a weighted extension of Multi-view Non-negative Matrix Factorization (NMF) to address the aforementioned drawbacks. The key idea is to learn query-specific generative model on the features of nearest-neighbors and tags using the proposed NMF-KNN approach which imposes consensus constraint on the coefficient matrices across different features. This results in coefficient vectors across features to be consistent and, thus, naturally solves the problem of feature fusion, while the weight matrices introduced in the proposed formulation alleviate the issue of dataset imbalance. Furthermore, our approach, being query-specific, is unaffected by addition of images and tags in a database. We tested our method on two datasets used for evaluation of image annotation and obtained competitive results.

@InProceedings{Kalayeh_2014_CVPR,author = {Kalayeh, Mahdi M. and Idrees, Haroon and Shah, Mubarak},title = {NMF-KNN: Image Annotation using Weighted Multi-view Non-negative Matrix Factorization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Given two images, we want to predict which exhibits a particular visual attribute more than the other---even when the two images are quite similar.  Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering.  To address these issues, we propose a local learning approach for fine-grained visual comparisons.  Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons.  We show how to identify these analogous pairs using learned metrics.  With results on three challenging datasets -- including a large newly curated dataset for fine-grained comparisons -- our method outperforms state-of-the-art methods for relative attribute prediction.

@InProceedings{Yu_2014_CVPR,author = {Yu, Aron and Grauman, Kristen},title = {Fine-Grained Visual Comparisons with Local Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: It is useful to automatically compare images based on their visual properties---to predict which image is brighter, more feminine, more blurry, etc.  However, comparative models are inherently more costly to train than their classification counterparts.  Manually labeling all pairwise comparisons is intractable, so which pairs should a human supervisor compare?  We explore active learning strategies for training relative attribute ranking functions, with the goal of requesting human comparisons only where they are most informative.  We introduce a novel criterion that requests a partial ordering for a set of examples that minimizes the total rank margin in attribute space, subject to a visual diversity constraint.  The setwise criterion helps amortize effort by identifying mutually informative comparisons, and the diversity requirement safeguards against requests a human viewer will find ambiguous.  We develop an efficient strategy to search for sets that meet this criterion.  On three challenging datasets and experiments with "live" online annotators, the proposed method outperforms both traditional passive learning as well as existing active rank learning methods.

@InProceedings{Liang_2014_CVPR,author = {Liang, Lucy and Grauman, Kristen},title = {Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Inferring Analogous Attributes

Abstract: The appearance of an attribute can vary considerably from class to class (e.g., a "fluffy" dog vs. a "fluffy" towel), making standard class-independent attribute models break down. Yet, training object-specific models for each attribute can be impractical, and defeats the purpose of using attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can infer new ones for object-attribute pairs unobserved during training. For example, even though the system has no labeled images of striped dogs, it can use its knowledge of other attributes and objects to tailor "stripedness" to the dog category. With two large-scale datasets, we demonstrate both the need for category-sensitive attributes as well as our method's successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer.

@InProceedings{Chen_2014_CVPR,author = {Chen, Chao-Yeh and Grauman, Kristen},title = {Inferring Analogous Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jungseock Joo, Weixin Li, Francis F. Steen, Song-Chun Zhu

Abstract: In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as "socially dominant," "energetic," and "trustworthy," and propose a hierarchical model that builds on the layer of syntactical attributes, such as "smile" and "waving hand," to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1,124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of investigations around mediated images, intersecting with media analysis, psychology, and political communication.

@InProceedings{Joo_2014_CVPR,author = {Joo, Jungseock and Li, Weixin and Steen, Francis F. and Zhu, Song-Chun},title = {Visual Persuasion: Inferring Communicative Intents of Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Winn Voravuthikunchai, Bruno Cremilleux, Frederic Jurie

Abstract: This paper introduces a novel image representation capturing feature  dependencies through the mining of meaningful combinations of visual   features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of  projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.

@InProceedings{Voravuthikunchai_2014_CVPR,author = {Voravuthikunchai, Winn and Cremilleux, Bruno and Jurie, Frederic},title = {Histograms of Pattern Sets for Image Classification and Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Hamid Izadinia, Fereshteh Sadeghi, Ali Farhadi

Abstract: A scene category imposes tight distributions over the kind of objects that might appear in the scene, the appearance of those objects and their layout. In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout. Our experimental evaluations show that our learned scene structures outperform state-of-the-art method of Deformable Part Models in detecting objects in a scene. Our scene structure provides a level of scene understanding that is amenable to deep visual inferences. The scene structures can also generate features that can later be used for scene categorization. Using these features, we also show promising results on scene categorization.

@InProceedings{Izadinia_2014_CVPR,author = {Izadinia, Hamid and Sadeghi, Fereshteh and Farhadi, Ali},title = {Incorporating Scene Context and Object Layout into Appearance Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mehmet Ersin Yumer, Won Chun, Ameesh Makadia

Abstract: We present a novel co-segmentation method for textured 3D shapes. Our algorithm takes a collection of textured shapes belonging to the same category and sparse annotations of foreground segments, and produces a joint dense segmentation of the shapes in the collection. We model the segments by a collectively trained Gaussian mixture model. The final model segmentation is formulated as an energy minimization across all models jointly, where intra-model edges control the smoothness and separation of model segments, and inter-model edges impart global consistency. We show promising results on two large real-world datasets, and also compare with previous shape-only 3D segmentation methods using publicly available datasets.

@InProceedings{Yumer_2014_CVPR,author = {Ersin Yumer, Mehmet and Chun, Won and Makadia, Ameesh},title = {Co-Segmentation of Textured 3D Shapes with Sparse Annotations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jiajun Wu, Yibiao Zhao, Jun-Yan Zhu, Siwei Luo, Zhuowen Tu

Abstract: Interactive segmentation, in which a user provides a bounding box to an object of interest for image segmentation, has been applied to a variety of applications in image editing, crowdsourcing, computer vision, and medical imaging. The challenge of this semi-automatic image segmentation task lies in dealing with the uncertainty of the foreground object within a bounding box. Here, we formulate the interactive segmentation problem as a multiple instance learning (MIL) task by generating positive bags from pixels of sweeping lines within a bounding box. We name this approach MILCut. We provide a justification to our formulation and develop an algorithm with significant performance and efficiency gain over existing state-of-the-art systems. Extensive experiments demonstrate the evident advantage of our approach.

@InProceedings{Wu_2014_CVPR,author = {Wu, Jiajun and Zhao, Yibiao and Zhu, Jun-Yan and Luo, Siwei and Tu, Zhuowen},title = {MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Zhuwen Li, Loong-Fah Cheong, Steven Zhiying Zhou

Abstract: While clustering has been well studied in the past decade, model selection has drawn less attention. This paper addresses both problems in a joint manner with an indicator matrix formulation, in which the clustering cost is penalized by a Frobenius inner product term and the group number estimation is achieved by a rank minimization. As affinity graphs generally contain positive edge values, a sparsity term is further added to avoid the trivial solution. Rather than adopting the conventional convex relaxation approach wholesale, we represent the original problem more faithfully by taking full advantage of the particular structure present in the optimization problem and solving it efficiently using the Alternating Direction Method of Multipliers. The highly constrained nature of the optimization provides our algorithm with the robustness to deal with the varying and often imperfect input affinity matrices arising from different applications and different group numbers. Evaluations on the synthetic data as well as two real world problems show the superiority of the method across a large variety of settings.

@InProceedings{Li_2014_CVPR,author = {Li, Zhuwen and Cheong, Loong-Fah and Zhiying Zhou, Steven},title = {SCAMS: Simultaneous Clustering and Model Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


How to Evaluate Foreground Maps?

Ran Margolin, Lihi Zelnik-Manor, Ayellet Tal

Abstract: The output of many algorithms in computer-vision is either non-binary maps or binary maps (e.g., salient object detection and object segmentation). Several measures have been suggested to evaluate the accuracy of these foreground maps. In this paper, we show that the most commonly-used measures for evaluating both non-binary maps and binary maps do not always provide a reliable evaluation. This includes the Area-Under-the-Curve measure,  the Average-Precision measure, the F-measure, and the evaluation measure of the PASCAL VOC segmentation challenge. We start by identifying three causes of inaccurate evaluation. We then propose a new measure that amends these flaws. An appealing property of our measure is being an intuitive generalization of the F-measure. Finally we propose four meta-measures to compare the adequacy of evaluation measures. We show via experiments that our novel measure is preferable.

@InProceedings{Margolin_2014_CVPR,author = {Margolin, Ran and Zelnik-Manor, Lihi and Tal, Ayellet},title = {How to Evaluate Foreground Maps?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andrew Kae, Benjamin Marlin, Erik Learned-Miller

Abstract: We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to model both the shape and temporal dependencies of an object in video. A typical approach for this task is the conditional random field (CRF), which can model local interactions among adjacent regions in a video frame. Recent work has shown how to incorporate a shape prior into a CRF for improving labeling performance, but it may be difficult to model temporal dependencies present in video by using this prior. The conditional restricted Boltzmann machine (CRBM) can model both shape and temporal dependencies, and has been used to learn walking styles from motion-capture data. In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos. In particular, we explore the task of labeling parts of complex face scenes from videos in the YouTube Faces Database (YFDB). Our combined model outperforms competitive baselines both qualitatively and quantitatively.

@InProceedings{Kae_2014_CVPR,author = {Kae, Andrew and Marlin, Benjamin and Learned-Miller, Erik},title = {The Shape-Time Random Field for Semantic Video Labeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, Alan L. Yuille

Abstract: In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasising the stereotypical concepts of saliency. The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.  Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth. With fixations and salient object being presented simultaneously,  we are able to bridge the gap between fixations and salient objects, and propose a novel method for salient object segmentation. Finally, we report significant benchmark progress on 3 existing datasets of segmenting salient objects.

@InProceedings{Li_2014_CVPR,author = {Li, Yin and Hou, Xiaodi and Koch, Christof and Rehg, James M. and Yuille, Alan L.},title = {The Secrets of Salient Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we propose a new methodology for segmenting non-rigid visual objects, where the search procedure is  onducted directly on a sparse low-dimensional manifold, guided by the classification results computed from a deep belief network. Our main contribution is the fact that we do not rely on the typical sub-division of segmentation tasks into rigid detection and non-rigid delineation. Instead, the non-rigid segmentation is performed directly, where points in the sparse low-dimensional can be mapped to an explicit contour representation in image space. Our proposal shows significantly smaller search and training complexities given that the dimensionality of the manifold is much smaller than the dimensionality of the search spaces for rigid detection and non-rigid delineation aforementioned, and that we no longer require a two-stage segmentation process. We focus on the problem of left ventricle endocardial segmentation from ultrasound images, and lip segmentation from frontal facial images using the extended Cohn-Kanade (CK+) database. Our experiments show that the use of sparse low dimensional manifolds reduces the search and training complexities of current segmentation approaches without a significant impact on the segmentation accuracy shown by state-of-the-art approaches.

@InProceedings{Nascimento_2014_CVPR,author = {Nascimento, Jacinto C. and Carneiro, Gustavo},title = {Non-rigid Segmentation using Sparse Low Dimensional Manifolds and Deep Belief Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of instance segmentation, in which a set of reference image/shape masks is used to find multiple objects. We design a novel CRF framework that jointly models object appearance, shape deformation, and object occlusion. To tackle the challenging MAP inference problem, we derive an alternating procedure that interleaves object segmentation and shape/appearance adaptation. We evaluate our method on two datasets with instance labels and show promising results.

@InProceedings{He_2014_CVPR,author = {He, Xuming and Gould, Stephen},title = {An Exemplar-based CRF for Multi-instance Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Simon Christoph Stein, Markus Schoeler, Jeremie Papon, Florentin Worgotter

Abstract: The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated data-sets. As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which -- with high accuracy -- represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.

@InProceedings{Stein_2014_CVPR,author = {Christoph Stein, Simon and Schoeler, Markus and Papon, Jeremie and Worgotter, Florentin},title = {Object Partitioning using Local Convexity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Active contour, especially in conjunction with prior-shape models, has become an important tool in image segmentation. However, most contour methods use shape priors based on similarity-shape analysis, i.e. analysis that is invariant to rotation, translation, and scale. In practice, the training shapes used for prior-shape models may be collected from viewing angles different from those for the test images and require invariance to a larger class of transformation. Using an elastic, affine-invariant shape modeling of planar curves, we propose an active contour algorithm in which the training and test shapes can be at arbitrary affine transformations, and the resulting segmentation is robust to perspective skews. We construct a shape space of affine-standardized curves and derive a statistical model for capturing class-specific shape variability. The active contour is then driven by the true gradient of a total energy composed of a data term, a smoothing term, and an affine-invariant shape-prior term. This framework is demonstrated using a number of examples involving the segmentation of occluded or noisy images of targets subject to perspective skew.

@InProceedings{Bryner_2014_CVPR,author = {Bryner, Darshan and Srivastava, Anuj},title = {Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jimei Yang, Simon Safar, Ming-Hsuan Yang

Abstract: We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation. MMBMs are essentially a class of Conditional Boltzmann Machines that model the joint distribution of hidden variables and output labels conditioned on input observations. In addition to image-to-label connections, we build direct image-to-hidden connections to facilitate global shape prediction, and thus derive a simple Iterated Conditional Modes algorithm for efficient maximum a posteriori inference. We formulate a max-margin objective function for discriminative training, and analyze the effects of different margin functions on learning. We evaluate MMBMs using three datasets against state-of-the-art methods to demonstrate the strength of the proposed algorithms.

@InProceedings{Yang_2014_CVPR,author = {Yang, Jimei and Safar, Simon and Yang, Ming-Hsuan},title = {Max-Margin Boltzmann Machines for Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Multiscale Combinatorial Grouping

Pablo Arbelaez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marques, Jitendra Malik

Abstract: We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates.

@InProceedings{Arbelaez_2014_CVPR,author = {Arbelaez, Pablo and Pont-Tuset, Jordi and Barron, Jonathan T. and Marques, Ferran and Malik, Jitendra},title = {Multiscale Combinatorial Grouping},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Popular figure-ground segmentation algorithms generate a pool of boundary-aligned segment proposals that can be used in subsequent object recognition engines. These algorithms can recover most image objects with high accuracy, but are usually computationally intensive since many graph cuts are computed with different enumerations of segment seeds. In this paper we propose an algorithm, RIGOR, for efficiently generating a pool of overlapping segment proposals in images. By precomputing a graph which can be used for parametric min-cuts over different seeds, we speed up the generation of the segment pool. In addition, we have made design choices that avoid extensive computations without losing performance. In particular, we demonstrate that the segmentation performance of our algorithm is slightly better than the state-of-the-art on the PASCAL VOC dataset, while being an order of magnitude faster.

@InProceedings{Humayun_2014_CVPR,author = {Humayun, Ahmad and Li, Fuxin and Rehg, James M.},title = {RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Steven Hickson, Stan Birchfield, Irfan Essa, Henrik Christensen

Abstract: We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.  Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation.  These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm.  Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. Due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm's ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBD data sets.

@InProceedings{Hickson_2014_CVPR,author = {Hickson, Steven and Birchfield, Stan and Essa, Irfan and Christensen, Henrik},title = {Efficient Hierarchical Graph-Based Segmentation of RGBD Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Recently, a concave optimization approach has been proposed to solve the robust point matching (RPM) problem. This method is globally optimal, but it requires that each model point has a counterpart in the data point set. Unfortunately, such a requirement may not be satisfied in certain applications when there are outliers in both point sets. To address this problem, we relax this condition and reduce the objective function of RPM to a function with  few nonlinear terms by eliminating the transformation variables. The resulting function, however, is no longer quadratic. We prove that it is still concave over the feasible region of point correspondence. The branch-and-bound (BnB) algorithm can then be used for optimization. To further improve the efficiency of the BnB algorithm whose bottleneck lies in the costly computation of the lower bound, we propose a new lower bounding scheme which has a k-cardinality linear assignment formulation and can be efficiently solved.  Experimental results show that  the proposed  algorithm outperforms state-of-the-arts in its robustness to disturbances and point matching accuracy.

@InProceedings{Lian_2014_CVPR,author = {Lian, Wei and Zhang, Lei},title = {Point Matching in the Presence of Outliers in Both Point Sets: A Concave Optimization Approach},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Feng-Ju Chang, Yen-Yu Lin, Kuang-Jui Hsu

Abstract: We present an approach MSIL-CRF that incorporates multiple instance learning (MIL) into conditional random fields (CRFs). It can generalize CRFs to work on training data with uncertain labels by the principle of MIL. In this work, it is applied to saving manual efforts on annotating training data for semantic segmentation. Specifically, we consider the setting in which the training dataset for semantic segmentation is a mixture of a few object segments and an abundant set of objects' bounding boxes. Our goal is to infer the unknown object segments enclosed by the bounding boxes so that they can serve as training data for semantic segmentation. To this end, we generate multiple segment hypotheses for each bounding box with the assumption that at least one hypothesis is close to the ground truth. By treating a bounding box as a bag with its segment hypotheses as structured instances, MSIL-CRF selects the most likely segment hypotheses by leveraging the knowledge derived from both the labeled and uncertain training data. The experimental results on the Pascal VOC segmentation task demonstrate that MSIL-CRF can provide effective alternatives to manually labeled segments for semantic segmentation.

@InProceedings{Chang_2014_CVPR,author = {Chang, Feng-Ju and Lin, Yen-Yu and Hsu, Kuang-Jui},title = {Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Adeel Mumtaz, Weichen Zhang, Antoni B. Chan

Abstract: We propose a joint foreground-background mixture model (FBM) that simultaneously performs background estimation and motion segmentation in complex dynamic scenes. Our FBM consist of a set of location-specific dynamic texture (DT) components, for modeling local background motion, and set of global DT components, for modeling consistent foreground motion. We derive an EM algorithm for estimating the parameters of the FBM. We also apply spatial constraints to the FBM using an Markov random field grid, and derive a corresponding variational approximation for inference. Unlike existing approaches to background subtraction, our FBM does not require a manually selected threshold or a separate training video. Unlike existing motion segmentation techniques, our FBM can segment foreground motions over complex background with mixed motions, and detect stopped objects. Since most dynamic scene datasets only contain videos with a single foreground object over a simple background, we develop a new challenging dataset with multiple foreground objects over complex dynamic backgrounds. In experiments, we show that jointly modeling the background and foreground segments with FBM yields significant improvements in accuracy on both background estimation and motion segmentation, compared to state-of-the-art methods.

@InProceedings{Mumtaz_2014_CVPR,author = {Mumtaz, Adeel and Zhang, Weichen and Chan, Antoni B.},title = {Joint Motion Segmentation and Background Estimation in Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we propose a technique for video object segmentation using patch seams across frames. Typically, seams, which are connected paths of low energy, are utilised for retargeting, where the primary aim is to reduce the image size while preserving  the salient image contents. Here, we adapt the formulation of seams for temporal label propagation. The energy function associated with the proposed video seams provides temporal linking of patches across frames, to accurately segment the object. The proposed energy function takes into account the similarity of patches along the seam, temporal consistency of motion and spatial coherency of seams. Label propagation is achieved with high fidelity in the critical boundary regions, utilising the proposed patch seams. To achieve this without additional overheads, we curtail the error propagation by formulating boundary regions as rough-sets. The proposed approach out-perform state-of-the-art supervised and unsupervised algorithms, on benchmark datasets.

@InProceedings{Ramakanth_2014_CVPR,author = {Avinash Ramakanth, S. and Venkatesh Babu, R.},title = {SeamSeg: Video Object Segmentation using Patch Seams},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Wallace Casaca, Luis Gustavo Nonato, Gabriel Taubin

Abstract: Seed-based image segmentation methods have gained much attention lately, mainly due to their good performance in segmenting complex images with little user interaction. Such popularity leveraged the development of many new variations of seed-based image segmentation techniques, which vary greatly regarding mathematical formulation and complexity. Most existing methods in fact rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima. In this work we present a novel framework for seed-based image segmentation that is mathematically simple, easy to implement, and guaranteed to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are kept closer to each other while big jumps are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed framework outperform state-of-the-art techniques in terms of quantitative quality metrics as well as qualitative visual results.

@InProceedings{Casaca_2014_CVPR,author = {Casaca, Wallace and Gustavo Nonato, Luis and Taubin, Gabriel},title = {Laplacian Coordinates for Seeded Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Scribbles in scribble-based interactive segmentation such as graph-cut are usually assumed to be perfectly accurate, i.e., foreground scribble pixels will never be segmented as background in the final segmentation. However, it can be hard to draw perfectly accurate scribbles, especially on fine structures of the image or on mobile touch-screen devices. In this paper, we propose a novel ratio energy function that tolerates errors in the user input while encouraging maximum use of the user input information. More specifically, the ratio energy aims to minimize the graph-cut energy while maximizing the user input respected in the segmentation.  The ratio energy function can be exactly optimized using an efficient iterated graph cut algorithm. The robustness of the proposed method is validated on the GrabCut dataset using both synthetic scribbles and manual scribbles. The experimental results show that the proposed algorithm is robust to the errors in the user input and preserves the "anchoring" capability of the user input.

@InProceedings{Bai_2014_CVPR,author = {Bai, Junjie and Wu, Xiaodong},title = {Error-tolerant Scribbles Based Interactive Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Nagesh Subbanna, Doina Precup, Tal Arbel

Abstract: In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients. An initial Bayesian tumour classification based on Gabor texture features permits subsequent computations to be focused on areas where the probability of tumour is deemed high. An iterative, multistage Markov Random Field (MRF) framework is then devised to classify the various tumour subclasses (e.g. edema, solid tumour, enhancing tumour and necrotic core). Specifically, an adapted, voxel-based MRF provides tumour candidates to a higher level, regional MRF, which then leverages both contextual texture information and relative spatial consistency of the tumour subclass positions to provide updated regional information down to the voxel-based MRF for further local refinement. The two stages iterate until convergence. Experiments are performed on publicly available, patient brain tumour images from the MICCAI 2012 [11] and 2013 [12] Brain Tumour Segmentation Challenges. The results demonstrate that the proposed method achieves the top performance in the segmentation of tumour cores and enhancing tumours, and performs comparably to the winners in other tumour categories.

@InProceedings{Subbanna_2014_CVPR,author = {Subbanna, Nagesh and Precup, Doina and Arbel, Tal},title = {Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, Henrik Aanaes

Abstract: The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.

@InProceedings{Jensen_2014_CVPR,author = {Jensen, Rasmus and Dahl, Anders and Vogiatzis, George and Tola, Engin and Aanaes, Henrik},title = {Large Scale Multi-view Stereopsis Evaluation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yinqiang Zheng, Shigeki Sugimoto, Imari Sato, Masatoshi Okutomi

Abstract: In this paper, we revisit the pose determination problem of a partially calibrated camera with unknown focal length, hereafter referred to as the PnPf problem, by using n (n ≥ 4) 3D-to-2D point correspondences. Our core contribution is to introduce the angle constraint and derive a compact bivariate polynomial equation for each point triplet. Based on this polynomial equation, we propose a truly general method for the PnPf problem, which is suited both to the minimal 4-point based RANSAC application, and also to large scale scenarios with thousands of points, irrespective of the 3D point configuration. In addition, by solving bivariate polynomial systems via the Sylvester resultant, our method is very simple and easy to implement. Its simplicity is especially obvious when one needs to develop a fast solver for the 4-point case on the basis of the characteristic polynomial technique. Experiment results have also demonstrated its superiority in accuracy and efficiency when compared with the existing state-of-the-art solutions.

@InProceedings{Zheng_2014_CVPR,author = {Zheng, Yinqiang and Sugimoto, Shigeki and Sato, Imari and Okutomi, Masatoshi},title = {A General and Simple Method for Camera Pose and Focal Length Determination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we present the first local descriptor designed for dynamic surfaces. A dynamic surface is a surface that can undergo non-rigid deformation (e.g., human body surface). Using state-of-the-art technology, details on dynamic surfaces such as cloth wrinkle or facial expression can be accurately reconstructed. Hence, various results (e.g., surface rigidity, or elasticity) could be derived by microscopic categorization of surface elements. We propose a timing-based descriptor to model local spatiotemporal variations of surface intrinsic properties. The low-level descriptor encodes gaps between local event dynamics of neighboring keypoints using timing structure of linear dynamical systems (LDS). We also introduce the bag-of-timings (BoT) paradigm for surface dynamics characterization. Experiments are performed on synthesized and real-world datasets. We show the proposed descriptor can be used for challenging dynamic surface classification and segmentation with respect to rigidity at surface keypoints.

@InProceedings{Tung_2014_CVPR,author = {Tung, Tony and Matsuyama, Takashi},title = {Timing-Based Local Descriptor for Dynamic Surfaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jonathan Ventura, Clemens Arth, Gerhard Reitmayr, Dieter Schmalstieg

Abstract: We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter.  This further generalization of the well-known absolute camera pose problem has applications in multi-frame loop closure.  While a well-calibrated camera rig has a fixed and known scale, camera trajectories produced by monocular motion estimation necessarily lack a scale estimate.   Thus, when performing loop closure in monocular visual odometry, or registering separate structure-from-motion reconstructions, we must estimate a seven degree-of-freedom similarity transform from corresponding observations.  Existing approaches solve this problem, in specialized configurations, by aligning 3D triangulated points or individual camera pose estimates.  Our approach handles general configurations of rays and points and directly estimates the full similarity transformation from the 2D-3D correspondences.  Four correspondences are needed in the minimal case, which has eight possible solutions.  The minimal solver can be used in a hypothesize-and-test architecture for robust transformation estimation.  Our solver also produces a least-squares estimate in the overdetermined case.  The approach is evaluated experimentally on synthetic and real datasets, and is shown to produce higher accuracy solutions to multi-frame loop closure than existing approaches.

@InProceedings{Ventura_2014_CVPR,author = {Ventura, Jonathan and Arth, Clemens and Reitmayr, Gerhard and Schmalstieg, Dieter},title = {A Minimal Solution to the Generalized Pose-and-Scale Problem},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yubin Kuang, Yinqiang Zheng, Kalle Astrom

Abstract: Algorithms for solving systems of polynomial equations are key components for solving geometry problems in computer vision. Fast and stable polynomial solvers are essential for numerous applications e.g. minimal problems or finding for all stationary points of certain algebraic errors. Recently, full symmetry in the polynomial systems has been utilized to simplify and speed up state-of-the-art polynomial solvers based on Grobner basis method. In this paper, we further explore partial symmetry (i.e. where the symmetry lies in a subset of the variables) in the polynomial systems. We develop novel numerical schemes to utilize such partial symmetry. We then demonstrate the advantage of our schemes in several computer vision problems. In both synthetic and real experiments, we show that utilizing partial symmetry allow us to obtain faster and more accurate polynomial solvers than the general solvers.

@InProceedings{Kuang_2014_CVPR,author = {Kuang, Yubin and Zheng, Yinqiang and Astrom, Kalle},title = {Partial Symmetry in Polynomial Systems and its Applications in Computer Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We present a novel solution to compute the relative pose of a generalized camera. Existing solutions are either not general, have too high computational complexity, or require too many correspondences, which impedes an efficient or accurate usage within Ransac schemes. We factorize the problem as a low-dimensional, iterative optimization over relative rotation only, directly derived from well-known epipolar constraints. Common generalized cameras often consist of camera clusters, and give rise to omni-directional landmark observations. We prove that our iterative scheme performs well in such practically relevant situations, eventually resulting in computational efficiency similar to linear solvers, and accuracy close to bundle adjustment, while using less correspondences. Experiments on both virtual and real multi-camera systems prove superior overall performance for robust, real-time multi-camera motion-estimation.

@InProceedings{Kneip_2014_CVPR,author = {Kneip, Laurent and Li, Hongdong},title = {Efficient Computation of Relative Pose for Multi-Camera Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: How much data do we need to describe a location? We explore this question in the context of 3D scene reconstructions created from running structure from motion on large Internet photo collections, where reconstructions can contain many millions of 3D points. We consider several methods for computing much more compact representations of such reconstructions for the task of location recognition, with the goal of maintaining good performance with very small models. In particular, we introduce a new method for computing compact models that takes into account both image-point relationships and feature distinctiveness, and we show that this method produces small models that yield better recognition performance than previous model reduction techniques.

@InProceedings{Cao_2014_CVPR,author = {Cao, Song and Snavely, Noah},title = {Minimal Scene Descriptions from Structure from Motion Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We describe an approach for simultaneous localization and calibration of a stream of range images. Our approach jointly optimizes the camera trajectory and a calibration function that corrects the camera's unknown nonlinear distortion. Experiments with real-world benchmark data and synthetic data show that our approach increases the accuracy of camera trajectories and geometric models estimated from range video produced by consumer-grade cameras.

@InProceedings{Zhou_2014_CVPR,author = {Zhou, Qian-Yi and Koltun, Vladlen},title = {Simultaneous Localization and Calibration: Self-Calibration of Consumer Depth Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andras Bodis-Szomoru, Hayko Riemenschneider, Luc Van Gool

Abstract: State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces. In turn, our interest lies in an approximate, but light-weight method that is better to consider for large-scale applications, such as urban scene reconstruction from ground-based images. We present a novel approach for producing dense reconstructions from multiple images and from the underlying sparse Structure-from-Motion (SfM) data in an efficient way. To overcome the problem of SfM sparsity and textureless areas, we assume piecewise planarity of man-made scenes and exploit both sparse visibility and a fast over-segmentation of the images. Reconstruction is formulated as an energy-driven, multi-view plane assignment problem, which we solve jointly over superpixels from all views while avoiding expensive photoconsistency computations. The resulting planar primitives -- defined by detailed superpixel boundaries -- are computed in about 10 seconds per image.

@InProceedings{Bodis-Szomoru_2014_CVPR,author = {Bodis-Szomoru, Andras and Riemenschneider, Hayko and Van Gool, Luc},title = {Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Behrooz Nasihatkon, Richard Hartley, Jochen Trumpf

Abstract: We study the theory of projective reconstruction for multiple projections from an arbitrary dimensional projective space into lower-dimensional spaces. This problem is important due to its applications in the analysis of dynamical scenes. The current theory, due to Hartley and Schaffalitzky, is based on the Grassmann tensor, generalizing the ideas of fundamental matrix, trifocal tensor and quadrifocal tensor used in the well-studied case of 3D to 2D projections. We present a theory whose point of departure is the projective equations rather than the Grassmann tensor. This is a better fit for the analysis of approaches such as bundle adjustment and projective factorization which seek to directly solve the projective equations. In a first step, we prove that there is a unique Grassmann tensor corresponding to each set of image points, a question that remained open in the work of Hartley and Schaffalitzky. Then, we prove that projective equivalence follows from the set of projective equations given certain conditions on the estimated camera-point setup or the estimated projective depths. Finally, we demonstrate how wrong solutions to the projective factorization problem can happen, and classify such degenerate solutions based on the zero patterns in the estimated depth matrix.

@InProceedings{Nasihatkon_2014_CVPR,author = {Nasihatkon, Behrooz and Hartley, Richard and Trumpf, Jochen},title = {On Projective Reconstruction In Arbitrary Dimensions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yilin Wang, Ke Wang, Enrique Dunn, Jan-Michael Frahm

Abstract: We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods.

@InProceedings{Wang_2014_CVPR,author = {Wang, Yilin and Wang, Ke and Dunn, Enrique and Frahm, Jan-Michael},title = {Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Adlane Habed, Danda Pani Paudel, Cedric Demonceaux, David Fofi

Abstract: We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters. Our method aims at estimating the Dual Absolute Quadric (DAQ) under the rank-3 and, optionally, camera centers chirality constraints. We employ the Branch-and-Prune paradigm and explore the space of only 5 parameters. Pruning in our method relies on solving Linear Matrix Inequality (LMI) feasibility and Generalized Eigenvalue (GEV) problems that solely depend upon the entries of the DAQ. These LMI and GEV problems are used to rule out branches in the search tree in which a quadric not satisfying the rank and chirality conditions on camera centers is guaranteed not to exist. The chirality LMI conditions are obtained by relying on the mild assumption that the camera undergoes a rotation of no more than 90 degrees between consecutive views. Furthermore, our method does not rely on calculating bounds on any particular cost function and hence can virtually optimize any objective while achieving global optimality in a very competitive running-time.

@InProceedings{Habed_2014_CVPR,author = {Habed, Adlane and Pani Paudel, Danda and Demonceaux, Cedric and Fofi, David},title = {Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jose Lezama, Rafael Grompone von Gioi, Gregory Randall, Jean-Michel Morel

Abstract: We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.

@InProceedings{Lezama_2014_CVPR,author = {Lezama, Jose and Grompone von Gioi, Rafael and Randall, Gregory and Morel, Jean-Michel},title = {Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Luis Ferraz, Xavier Binefa, Francesc Moreno-Noguer

Abstract: We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point (PnP) problem. The main advantages of our solution are twofold: first, it in- tegrates the outlier rejection within the pose estimation pipeline with a negligible computational overhead; and sec- ond, its scalability to arbitrarily large number of correspon- dences. Given a set of 3D-to-2D matches, we formulate pose estimation problem as a low-rank homogeneous sys- tem where the solution lies on its 1D null space. Outlier correspondences are those rows of the linear system which perturb the null space and are progressively detected by projecting them on an iteratively estimated solution of the null space. Since our outlier removal process is based on an algebraic criterion which does not require computing the full-pose and reprojecting back all 3D points on the image plane at each step, we achieve speed gains of more than 100× compared to RANSAC strategies. An extensive exper- imental evaluation will show that our solution yields accu- rate results in situations with up to 50% of outliers, and can process more than 1000 correspondences in less than 5ms.

@InProceedings{Ferraz_2014_CVPR,author = {Ferraz, Luis and Binefa, Xavier and Moreno-Noguer, Francesc},title = {Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The prevalent approach to image-based localization is matching interest points detected in the query image to a sparse 3D point cloud representing the known world. The obtained correspondences are then used to recover a precise camera pose. The state-of-the-art in this field often ignores the availability of a set of 2D descriptors per 3D point, for example by representing each 3D point by only its centroid. In this paper we demonstrate that these sets contain useful information that can be exploited by formulating matching as a discriminative classification problem. Since memory demands and computational complexity are crucial in such a setup, we base our algorithm on the efficient and effective random fern principle. We propose an extension which projects features to fern-specific embedding spaces, which yields improved matching rates in short runtime. Experiments first show that our novel formulation provides improved matching performance in comparison to the standard nearest neighbor approach and that we outperform related randomization methods in our localization scenario.

@InProceedings{Donoser_2014_CVPR,author = {Donoser, Michael and Schmalstieg, Dieter},title = {Discriminative Feature-to-Point Matching in Image-Based Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we present a novel refractive calibration method for an underwater stereo camera system where both cameras are looking through multiple parallel flat refractive interfaces. At the heart of our method is an important finding that the thickness of the interface can be estimated from a set of pixel correspondences in the stereo images when the refractive axis is given. To our best knowledge, such a finding has not been studied or reported. Moreover, by exploring the search space for the refractive axis and using reprojection error as a measure, both the refractive axis and the thickness of the interface can be recovered simultaneously. Our method does not require any calibration target such as a checkerboard pattern which may be difficult to manipulate when the cameras are deployed deep undersea. The implementation of our method is  simple. In particular, it only requires solving a set of linear equations of the form Ax = b and applies sparse bundle adjustment to refine the initial estimated results. Extensive experiments have been carried out which include simulations with and without outliers to verify the correctness of our method as well as to test its robustness to noise and outliers. The results of real experiments are also provided. The accuracy of our results is comparable to that of a state-of-the-art method that requires known 3D geometry of a scene.

@InProceedings{Chen_2014_CVPR,author = {Chen, Xida and Yang, Yee-Hong},title = {Two-View Camera Housing Parameters Calibration for Multi-Layer Flat Refractive Interface},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Linus Svarm, Olof Enqvist, Magnus Oskarsson, Fredrik Kahl

Abstract: We consider the problem of localizing a novel image in a large 3D model. In principle, this is just an instance of camera pose estimation, but the scale introduces some challenging problems. For one, it makes the correspondence problem very difficult and it is likely that there will be a significant rate of outliers to handle.   In this paper we use recent theoretical as well as technical advances to tackle these problems. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite more than 99% of outlier correspondences.

@InProceedings{Svarm_2014_CVPR,author = {Svarm, Linus and Enqvist, Olof and Oskarsson, Magnus and Kahl, Fredrik},title = {Accurate Localization and Pose Estimation for Large 3D Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions.  Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models.

@InProceedings{Nowozin_2014_CVPR,author = {Nowozin, Sebastian},title = {Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Gim Hee Lee, Marc Pollefeys, Friedrich Fraundorfer

Abstract: In this paper, we present our minimal 4-point and linear 8-point algorithms to estimate the relative pose of a multi-camera system with known vertical directions, i.e. known absolute roll and pitch angles. We solve the minimal 4-point algorithm with the hidden variable resultant method and show that it leads to an 8-degree univariate polynomial that gives up to 8 real solutions. We identify a degenerated case from the linear 8-point algorithm when it is solved with the standard Singular Value Decomposition (SVD) method and adopt a simple alternative solution which is easy to implement. We show that our proposed algorithms can be efficiently used within RANSAC for robust estimation. We evaluate the accuracy of our proposed algorithms by comparisons with various existing algorithms for the multi-camera system on simulations and show the feasibility of our proposed algorithms with results from multiple real-world datasets.

@InProceedings{Lee_2014_CVPR,author = {Hee Lee, Gim and Pollefeys, Marc and Fraundorfer, Friedrich},title = {Relative Pose Estimation for a Multi-Camera System with Known Vertical Direction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Thierry Guillemot, Andres Almansa, Tamy Boubekeur

Abstract: Gaussian Mixture Models have become one of the major tools in modern statistical image processing, and allowed performance breakthroughs in patch-based image denoising and restoration problems. Nevertheless, their adoption level was kept relatively low because of the computational cost associated to learning such models on large image databases. This work provides a flexible and generic tool for dealing with such models without the computational penalty or parameter tuning difficulties associated to a naïve implementation of GMM-based image restoration tasks. It does so by organising the data manifold in a hirerachical multiscale structure (the Covariance Tree) that can be queried at various scale levels around any point in feature-space. We start by explaining how to construct a Covariance Tree from a subset of the input data, how to enrich its statistics from a larger set in a streaming process, and how to query it efficiently, at any scale. We then demonstrate its usefulness on several applications, including non-local image filtering, data-driven denoising, reconstruction from random samples and surface modeling from unorganized 3D points sets.

@InProceedings{Guillemot_2014_CVPR,author = {Guillemot, Thierry and Almansa, Andres and Boubekeur, Tamy},title = {Covariance Trees for 2D and 3D Processing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}