Chen Chen, Yeqing Li, Wei Liu, Junzhou Huang

Abstract: In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location. Different from previous methods, we do not make any assumption about the upsampled multispectral image, but only assume that the fused image after downsampling should be close to the original multispectral image. This is a severely ill-posed problem and a dynamic gradient sparsity penalty is thus proposed for regularization. Incorporating the intra- correlations of different bands, this penalty can effectively exploit the prior information (e.g. sharp boundaries) from the panchromatic image. A new convex optimization algorithm is proposed to efficiently solve this problem. Extensive experiments on four multispectral datasets demonstrate that the proposed method significantly outperforms the state-of-the-arts in terms of both spatial and spectral qualities.

@InProceedings{Chen_2014_CVPR,author = {Chen, Chen and Li, Yeqing and Liu, Wei and Huang, Junzhou},title = {Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Most state-of-the-art dynamic scene deblurring methods based on accurate motion segmentation assume that motion blur is small or that the specific type of motion causing the blur is known. In this paper, we study a motion segmentation-free dynamic scene deblurring method, which is unlike other conventional methods. When the motion can be approximated to linear motion that is locally (pixel-wise) varying, we can handle various types of blur caused by camera shake, including out-of-plane motion, depth variation, radial distortion, and so on. Thus, we propose a new energy model simultaneously estimating motion flow and the latent image based on robust total variation (TV)-L1 model. This approach is necessary to handle abrupt changes in motion without segmentation. Furthermore, we address the problem of the traditional coarse-to-fine deblurring framework, which gives rise to artifacts when restoring small structures with distinct motion. We thus propose a novel kernel re-initialization method which reduces the error of motion flow propagated from a coarser level. Moreover, a highly effective convex optimization-based solution mitigating the computational difficulties of the TV-L1 model is established. Comparative experimental results on challenging real blurry images demonstrate the efficiency of the proposed method.

@InProceedings{Kim_2014_CVPR,author = {Hyun Kim, Tae and Mu Lee, Kyoung},title = {Segmentation-Free Dynamic Scene Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Many state-of-the-art image restoration approaches do not scale well to larger images, such as megapixel images common in the consumer segment. Computationally expensive optimization is often the culprit. While efficient alternatives exist, they have not reached the same level of image quality. The goal of this paper is to develop an effective approach to image restoration that offers both computational efficiency and high restoration quality. To that end we propose shrinkage fields, a random field-based architecture that combines the image model and the optimization algorithm in a single unit. The underlying shrinkage operation bears connections to wavelet approaches, but is used here in a random field context. Computational efficiency is achieved by construction through the use of convolution and DFT as the core components; high restoration quality is attained through loss-based training of all model parameters and the use of a cascade architecture. Unlike heavily engineered solutions, our learning approach can be adapted easily to different trade-offs between efficiency and image quality. We demonstrate state-of-the-art restoration results with high levels of computational efficiency, and significant speedup potential through inherent parallelism.

@InProceedings{Schmidt_2014_CVPR,author = {Schmidt, Uwe and Roth, Stefan},title = {Shrinkage Fields for Effective Image Restoration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andrew Owens, Connelly Barnes, Alex Flint, Hanumant Singh, William Freeman

Abstract: We address the problem of camouflaging a 3D object from the many viewpoints that one might see it from. Given photographs of an object's surroundings, we produce a surface texture that will make the object difficult for a human to detect. To do this, we introduce several background matching algorithms that attempt to make the object look like whatever is behind it. Of course, it is impossible to exactly match the background from every possible viewpoint. Thus our models are forced to make trade-offs between different perceptual factors, such as the conspicuousness of the occlusion boundaries and the amount of texture distortion. We use experiments with human subjects to evaluate the effectiveness of these models for the task of camouflaging a cube, finding that they significantly outperform naïve strategies.

@InProceedings{Owens_2014_CVPR,author = {Owens, Andrew and Barnes, Connelly and Flint, Alex and Singh, Hanumant and Freeman, William},title = {Camouflaging an Object from Many Viewpoints},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Saliency prediction typically relies on hand-crafted (multiscale) features that are combined in different ways to form a "master" saliency map, which encodes local image conspicuity. Recent improvements to the state of the art on standard benchmarks such as MIT1003 have been achieved mostly by incrementally adding more and more hand-tuned features (such as car or face detectors) to existing models. In contrast, we here follow an entirely automatic data-driven approach that performs a large-scale search for optimal features. We identify those instances of a richly-parameterized bio-inspired model family (hierarchical neuromorphic networks) that successfully predict image saliency. Because of the high dimensionality of this parameter space, we use automated hyperparameter optimization to efficiently guide the search. The optimal blend of such multilayer features combined with a simple linear classifier achieves excellent performance on several image saliency benchmarks. Our models outperform the state of the art on MIT1003, on which features and classifiers are learned. Without additional training, these models generalize well to two other image saliency data sets, Toronto and NUSEF, despite their different image content. Finally, our algorithm scores best of all the 23 models evaluated to date on the MIT300 saliency challenge, which uses a hidden test set to facilitate an unbiased comparison.

@InProceedings{Vig_2014_CVPR,author = {Vig, Eleonora and Dorr, Michael and Cox, David},title = {Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Song Lu, Vijay Mahadevan, Nuno Vasconcelos

Abstract: In diffusion-based saliency detection, an image is partitioned into superpixels and mapped to a graph, with superpixels as nodes and edge strengths proportional to superpixel similarity. Saliency information is then propagated over the graph using a diffusion process, whose equilibrium state yields the object saliency map. The optimal solution is the product of a propagation matrix and a saliency seed vector that contains a prior saliency assessment. This is obtained from either a bottom-up saliency detector or some heuristics. In this work, we propose a method to learn optimal seeds for object saliency. Two types of features are computed per superpixel: the bottom-up saliency of the superpixel region and a set of mid-level vision features informative of how likely the superpixel is to belong to an object. The combination of features that best discriminates between object and background saliency is then learned, using a large-margin formulation of the discriminant saliency principle. The propagation of the resulting saliency seeds, using a diffusion process, is finally shown to outperform the state of the art on a number of salient object detection datasets.

@InProceedings{Lu_2014_CVPR,author = {Lu, Song and Mahadevan, Vijay and Vasconcelos, Nuno},title = {Learning Optimal Seeds for Diffusion-based Salient Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Wangjiang Zhu, Shuang Liang, Yichen Wei, Jian Sun

Abstract: Recent progresses in salient object detection have exploited the boundary prior, or background information, to assist other saliency cues such as contrast, achieving state-of-the-art results. However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic. In this work, we present new methods to address these issues. First, we propose a robust background measure, called boundary connectivity. It characterizes the spatial layout of image regions with respect to image boundaries and is much more robust. It has an intuitive geometrical interpretation and presents unique benefits that are absent in previous saliency measures. Second, we propose a principled optimization framework to integrate multiple low level cues, including our background measure, to obtain clean and uniform saliency maps. Our formulation is intuitive, efficient and achieves state-of-the-art results on several benchmark datasets.

@InProceedings{Zhu_2014_CVPR,author = {Zhu, Wangjiang and Liang, Shuang and Wei, Yichen and Sun, Jian},title = {Saliency Optimization from Robust Background Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Saliency Detection on Light Field

Nianyi Li, Jinwei Ye, Yu Ji, Haibin Ling, Jingyi Yu

Abstract: Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions. We explore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. We show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. We further develop a new saliency detection algorithm tailored for light fields. To validate our approach, we acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that our saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions,  etc., and achieve high accuracy and robustness.

@InProceedings{Li_2014_CVPR,author = {Li, Nianyi and Ye, Jinwei and Ji, Yu and Ling, Haibin and Yu, Jingyi},title = {Saliency Detection on Light Field},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT). This theory states that attention propagates from the top level of the visual hierarchy that processes gist and abstract information of input, to the bottom level that processes local details. Inspired by the theory, we develop a computational model for saliency detection in images. First, the original image is downsampled to different scales to constitute a pyramid. Then, saliency on each layer is obtained by image super-resolution reconstruction from the layer above, which is defined as unpredictability from this coarse-to-fine reconstruction. Finally, saliency on each layer of the pyramid is fused into stochastic fixations through a probabilistic model, where attention initiates from the top layer and propagates downward through the pyramid.  Extensive experiments on two standard eye-tracking datasets show that the proposed method can achieve competitive results with state-of-the-art models.

@InProceedings{Shi_2014_CVPR,author = {Shi, Tianlin and Liang, Ming and Hu, Xiaolin},title = {A Reverse Hierarchy Model for Predicting Eye Fixations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Miao Hua, Xiaohui Bie, Minying Zhang, Wencheng Wang

Abstract: Gradient domain methods are popular for image processing. However, these methods even the edge-preserving ones cannot preserve edges well in some cases. In this paper, we present new constraints explicitly to better preserve edges for general gradient domain image filtering and theoretically analyse why these constraints are edge-aware. Our edge-aware constraints are easy to implement, fast to compute and can be seamlessly integrated into the general gradient domain optimization framework. The improved framework can better preserve edges while maintaining similar image filtering effects as the original image filters. We also demonstrate the strength of our edge-aware constraints on various applications such as image smoothing, image colorization and Poisson image cloning.

@InProceedings{Hua_2014_CVPR,author = {Hua, Miao and Bie, Xiaohui and Zhang, Minying and Wang, Wencheng},title = {Edge-aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation. But it is hard to be accelerated due to the spatially varying weight and the median property. We propose a few efficient schemes to reduce computation complexity from O(r^2) to O(r) where r is the kernel size. Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access. The effectiveness of these schemes is demonstrated on optical flow estimation, stereo matching, structure-texture separation, image filtering, to name a few. The running time is largely shortened from several minutes to less than 1 second. The source code is provided in the project website.

@InProceedings{Zhang_2014_CVPR,author = {Zhang, Qi and Xu, Li and Jia, Jiaya},title = {100+ Times Faster Weighted Median Filter (WMF)},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Super-Resolving Noisy Images

Abhishek Singh, Fatih Porikli, Narendra Ahuja

Abstract: Our goal is to obtain a noise-free, high resolution (HR) image, from an observed, noisy, low resolution (LR) image. The conventional approach of preprocessing the image with a denoising algorithm, followed by applying a super-resolution (SR) algorithm, has an important limitation: Along with noise, some high frequency content of the image (particularly textural detail) is invariably lost during the denoising step. This 'denoising loss' restricts the performance of the subsequent SR step, wherein the challenge is to synthesize such textural details. In this paper, we show that high frequency content in the noisy image (which is ordinarily removed by denoising algorithms) can be effectively used to obtain the missing textural details in the HR domain. To do so, we first obtain HR versions of both the noisy and the denoised images, using a patch-similarity based SR algorithm. We then show that by taking a convex combination of orientation and frequency selective bands of the noisy and the denoised HR images, we can obtain a desired HR image where (i) some of the textural signal lost in the denoising step is effectively recovered in the HR domain, and (ii) additional textures can be easily synthesized by appropriately constraining the parameters of the convex combination. We show that this part-recovery and part-synthesis of textures through our algorithm yields HR images that are visually more pleasing than those obtained using the conventional processing pipeline. Furthermore, our results show a consistent improvement in numerical metrics, further corroborating the ability of our algorithm to recover lost signal.

@InProceedings{Singh_2014_CVPR,author = {Singh, Abhishek and Porikli, Fatih and Ahuja, Narendra},title = {Super-Resolving Noisy Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xiaowu Chen, Dongqing Zou, Jianwei Li, Xiaochun Cao, Qinping Zhao, Hao Zhang

Abstract: We introduce a method of sparse dictionary learning for edit propagation of high-resolution images or video. Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a compact set of representative samples (or features) and perform edit propagation on the samples instead. The sparse set of samples provides an intrinsic basis for an input image, and the coding coefficients capture the linear relationship between all pixels and the samples. The representative set of samples is then optimized by a novel scheme which maximizes the KL-divergence between each sample pair to remove redundant samples. We show several applications of sparsity-based edit propagation including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. We demonstrate that with a sample-to-pixel ratio in the order of 0.01%, signifying a significant reduction on memory consumption, our method still maintains a high-degree of visual fidelity.

@InProceedings{Chen_2014_CVPR,author = {Chen, Xiaowu and Zou, Dongqing and Li, Jianwei and Cao, Xiaochun and Zhao, Qinping and Zhang, Hao},title = {Sparse Dictionary Learning for Edit Propagation of High-Resolution Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng

Abstract: As a convex relaxation of the low rank matrix factorization problem, the nuclear norm minimization has been attracting significant research interest in recent years. The standard nuclear norm minimization regularizes each singular value equally to pursue the convexity of the objective function. However, this greatly restricts its capability and flexibility in dealing with many practical problems (e.g., denoising), where the singular values have clear physical meanings and should be treated differently. In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights. The solutions of the WNNM problem are analyzed under different weighting conditions. We then apply the proposed WNNM algorithm to image denoising by exploiting the image nonlocal self-similarity. Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality.

@InProceedings{Gu_2014_CVPR,author = {Gu, Shuhang and Zhang, Lei and Zuo, Wangmeng and Feng, Xiangchu},title = {Weighted Nuclear Norm Minimization with Application to Image Denoising},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: It is often desirable to evaluate images quality with a perceptually relevant measure that does not require a reference image.  Recent approaches to this problem use human provided quality scores with machine learning to learn a measure. The biggest hurdles to these efforts are: 1) the difficulty of generalizing across diverse types of distortions and 2) collecting the enormity of human scored training data that is needed to learn the measure. We present a new blind image quality measure that addresses these difficulties by learning a robust, nonlinear kernel regression function using a rectifier neural network.  The method is pre-trained with unlabeled data and fine-tuned with labeled data.  It generalizes across a large set of images and distortion types without the need for a large amount of labeled data.  We evaluate our approach on two benchmark datasets and show that it not only outperforms the current state of the art in blind image quality estimation, but also outperforms the state of the art in non-blind measures.  Furthermore, we show that our semi-supervised approach is robust to using varying amounts of labeled data.

@InProceedings{Tang_2014_CVPR,author = {Tang, Huixuan and Joshi, Neel and Kapoor, Ashish},title = {Blind Image Quality Assessment using Semi-supervised Rectifier Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Kurtosis of 1D projections provides important statistical characteristics of natural images. In this work, we first provide a theoretical underpinning to a recently observed phenomenon known as projection kurtosis concentration that the kurtosis of natural images over different band-pass channels tend to concentrate around a typical value.  Based on this analysis, we further describe a new method to estimate the covariance matrix of correlated Gaussian noise from a noise corrupted image using random band-pass filters. We demonstrate the effectiveness of our blind noise covariance matrix estimation method on natural images.

@InProceedings{Zhang_2014_CVPR,author = {Zhang, Xing and Lyu, Siwei},title = {Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Separable Kernel for Image Deblurring

Lu Fang, Haifeng Liu, Feng Wu, Xiaoyan Sun, Houqiang Li

Abstract: In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system. Specifically, we decompose a blur kernel into three individual descriptors (trajectory, intensity and point spread function) so that they can be optimized separately. To demonstrate the advantages, we extract one-pixel-width trajectories of blur kernels and propose a random perturbation algorithm to optimize them but still keeping their continuity. For many cases, where current deblurring approaches fall into local minimum, excellent deblurred results and correct blur kernels can be obtained by individually optimizing the kernel trajectories. Our work strongly suggests that more constraints and priors should be introduced to blur kernels in solving the deblurring problem because blur kernels have lower dimensions than images.

@InProceedings{Fang_2014_CVPR,author = {Fang, Lu and Liu, Haifeng and Wu, Feng and Sun, Xiaoyan and Li, Houqiang},title = {Separable Kernel for Image Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layer-based solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.

@InProceedings{Hu_2014_CVPR,author = {Hu, Zhe and Xu, Li and Yang, Ming-Hsuan},title = {Joint Depth Estimation and Camera Shake Removal from Single Blurry Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang

Abstract: We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.

@InProceedings{Pan_2014_CVPR,author = {Pan, Jinshan and Hu, Zhe and Su, Zhixun and Yang, Ming-Hsuan},title = {Deblurring Text Images via L0-Regularized Intensity and Gradient Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.

@InProceedings{Zhu_2014_CVPR,author = {Zhu, Yu and Zhang, Yanning and Yuille, Alan L.},title = {Single Image Super-resolution using Deformable Patches},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

@InProceedings{Perrone_2014_CVPR,author = {Perrone, Daniele and Favaro, Paolo},title = {Total Variation Blind Deconvolution: The Devil is in the Details},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The capture of multiple images is a simple way to increase the chance of capturing a good photo with a light-weight hand-held camera, for which the camera-shake blur is typically a nuisance problem. The naive approach of selecting the  single best  captured photo as output does not take full advantage of all the observations. Conventional multi-image blind deblurring methods can take all observations as input but  usually require  the multiple images are well aligned. However, the multiple blurry images captured in the presence of camera shake are rarely free from mis-alignment. Registering multiple blurry images is a challenging task due to the presence of blur while deblurring of multiple blurry images requires accurate alignment, leading to an intrinsically  coupled problem. In this paper, we propose  a blind multi-image restoration method which can achieve  joint alignment, non-uniform  deblurring, together with resolution enhancement from multiple low-quality images. Experiments on several real-world images with comparison to some previous methods validate the effectiveness of the proposed method.

@InProceedings{Zhang_2014_CVPR,author = {Zhang, Haichao and Carin, Lawrence},title = {Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution-Enhancement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Huanjing Yue, Xiaoyan Sun, Jingyu Yang, Feng Wu

Abstract: In this paper, we propose a novel two-step scheme to filter heavy noise from images with the assistance of retrieved Web images. There are two key technical contributions in our scheme. First, for every noisy image block, we build two three dimensional (3D) data cubes by using similar blocks in retrieved Web images and similar nonlocal blocks within the noisy image, respectively. To better use their correlations, we propose different denoising strategies. The denoising in the 3D cube built upon the retrieved images is performed as median filtering in the spatial domain, whereas the denoising in the other 3D cube is performed in the frequency domain. These two denoising results are then combined in the frequency domain to produce a denoising image. Second, to handle heavy noise, we further propose using the denoising image to improve image registration of the retrieved Web images, 3D cube building, and the estimation of filtering parameters in the frequency domain. Afterwards, the proposed denoising is performed on the noisy image again to generate the final denoising result. Our experimental results show that when the noise is high, the proposed scheme is better than BM3D by more than 2 dB in PSNR and the visual quality improvement is clear to see.

@InProceedings{Yue_2014_CVPR,author = {Yue, Huanjing and Sun, Xiaoyan and Yang, Jingyu and Wu, Feng},title = {CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper presents a novel guided image filtering method using multipoint local polynomial approximation (LPA) with range guidance. In our method, the LPA is extended from a pointwise model into a multipoint model for reliable  filtering and better preserving image spatial variation which usually contains the essential information in the input image. In addition, we develop a scheme with constant computational complexity (invariant to the size of filtering kernel) for generating a spatial adaptive support region around a point. By using the hybrid of the local polynomial model and color/intensity based range guidance, the proposed method not only preserves edges but also does a much better job in preserving spatial variation than existing popular filtering methods. Our method proves to be effective in a number of applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Experimental results show that our method produces better results than state-of-the-art methods and it is also computationally efficient.

@InProceedings{Tan_2014_CVPR,author = {Tan, Xiao and Sun, Changming and Pham, Tuan D.},title = {Multipoint Filtering with Local Polynomial Approximation and Range Guidance},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xiangqi Huang, Bo Zheng, Takeshi Masuda, Katsushi Ikeuchi

Abstract: Laser range sensors are often demanded to mount on a moving platform for achieving the good efficiency of 3D reconstruction. However, such moving systems often suffer from the difficulty of matching the distorted range scans. In this paper, we propose novel 3D features which can be robustly extracted and matched even for the distorted 3D surface captured by a moving system. Our feature extraction employs Morse theory to construct Morse functions which capture the critical points approximately invariant to the 3D surface distortion. Then for each critical point, we extract support regions with the maximally stable region defined by extremal region or disconnectivity. Our feature description is designed as two steps: 1) we normalize the detected local regions to canonical shapes for robust matching; 2) we encode each key point with multiple vectors at different Morse function values. In experiments, we demonstrate that the proposed 3D features achieve substantially better performance for distorted surface matching than the state-of-the-art methods.

@InProceedings{Huang_2014_CVPR,author = {Huang, Xiangqi and Zheng, Bo and Masuda, Takeshi and Ikeuchi, Katsushi},title = {Robust 3D Features for Matching between Distorted Range Scans Captured by Moving Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang

Abstract: As compared to the conventional RGB or gray-scale images, multispectral images (MSI) can deliver more faithful representation for real scenes, and enhance the performance of many computer vision tasks. In practice, however, an MSI is always corrupted by various noises. In this paper we propose an effective MSI denoising approach by combinatorially considering two intrinsic characteristics underlying an MSI: the nonlocal similarity over space and the global correlation across spectrum. In specific, by explicitly considering spatial self-similarity of an MSI we construct a nonlocal tensor dictionary learning model with a group-block-sparsity constraint, which makes similar full-band patches (FBP) share the same atoms from the spatial and spectral dictionaries. Furthermore, through exploiting spectral correlation of an MSI and assuming over-redundancy of dictionaries, the constrained nonlocal MSI dictionary learning model can be decomposed into a series of unconstrained low-rank tensor approximation problems, which can be readily solved by off-the-shelf higher order statistics. Experimental results show that our method outperforms all state-of-the-art MSI denoising methods under comprehensive quantitative performance measures.

@InProceedings{Peng_2014_CVPR,author = {Peng, Yi and Meng, Deyu and Xu, Zongben and Gao, Chenqiang and Yang, Yi and Zhang, Biao},title = {Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns. The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations. The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns. In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern. Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat. A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification. A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy. Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail.

@InProceedings{Pritts_2014_CVPR,author = {Pritts, James and Chum, Ondrej and Matas, Jiri},title = {Detection, Rectification and Segmentation of Coplanar Repeated Patterns},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Discriminative Blur Detection Features

Abstract: Ubiquitous image blur brings out a practically important question – what are effective features to differentiate between blurred and unblurred image regions. We address it by studying a few blur feature representations in image gradient, Fourier domain, and data-driven local filters. Unlike previous methods, which are often based on restoration mechanisms, our features are constructed to enhance discriminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth. Our results are applied to several applications, including blur region segmentation, deblurring, and blur magnification.

@InProceedings{Shi_2014_CVPR,author = {Shi, Jianping and Xu, Li and Jia, Jiaya},title = {Discriminative Blur Detection Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Marcelo Cicconet, Davi Geiger, Kristin C. Gunsalus, Michael Werman

Abstract: We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients. We compute such a coefficient for every pair of pixels, and group them in a 6-dimensional histogram. By marginalizing the HMSC in various ways, we develop algorithms for a range of applications: detection of nearly-circular cells; location of the main axis of reflection symmetry; detection of cell-division in movies of developing embryos; detection of worm-tips and indirect cell-counting via supervised classification. Our approach generalizes a series of histogram-related methods, and the proposed algorithms perform with state-of-the-art accuracy.

@InProceedings{Cicconet_2014_CVPR,author = {Cicconet, Marcelo and Geiger, Davi and Gunsalus, Kristin C. and Werman, Michael},title = {Mirror Symmetry Histograms for Capturing Geometric Properties in Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Haze is one of the major factors that degrade outdoor images. Removing haze from a single image is known to be severely ill-posed, and assumptions made in previous methods do not hold in many situations. In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing. We show that the dark-channel feature is the most informative one for this task, which confirms the observation of He et al. [8] from a learning perspective, while other haze-relevant features also contribute significantly in a complementary way. We also find that surprisingly, the synthetic hazy image patches we use for feature investigation serve well as training data for realworld images, which allows us to train specific models for specific applications. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.

@InProceedings{Tang_2014_CVPR,author = {Tang, Ketan and Yang, Jianchao and Wang, Jue},title = {Investigating Haze-relevant Features in A Learning Framework for Image Dehazing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang

Abstract: We present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement.

@InProceedings{Yan_2014_CVPR,author = {Yan, Jianzhou and Lin, Stephen and Bing Kang, Sing and Tang, Xiaoou},title = {A Learning-to-Rank Approach for Image Color Enhancement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Zhengying Chen, Tingting Jiang, Yonghong Tian

Abstract: As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task. In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. Not like traditional image quality assessment approaches, we focus on the relative quality ranking between enhanced images rather than giving an absolute quality score for a single enhanced image. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images. A rank function is trained to fit the subjective assessment results, and can be used to predict ranks of new enhanced images which indicate the relative quality of enhancement algorithms. The experimental results show that our proposed approach statistically outperforms state-of-the-art general-purpose NR-IQA algorithms.

@InProceedings{Chen_2014_CVPR,author = {Chen, Zhengying and Jiang, Tingting and Tian, Yonghong},title = {Quality Assessment for Comparing Image Enhancement Algorithms},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Shadow Removal from Single RGB-D Images

Yao Xiao, Efstratios Tsougenis, Chi-Keung Tang

Abstract: We present the first automatic method to remove shadows from single RGB-D images. Using normal cues directly derived from depth, we can remove hard and soft shadows while preserving surface texture and shading. Our key assumption is: pixels with similar normals, spatial locations and chromaticity should have similar colors. A modified nonlocal matching is used to compute a shadow confidence map that localizes well hard shadow boundary, thus handling hard and soft shadows within the same framework. We compare our results produced using state-of-the-art shadow removal on single RGB images, and intrinsic image decomposition on standard RGB-D datasets.

@InProceedings{Xiao_2014_CVPR,author = {Xiao, Yao and Tsougenis, Efstratios and Tang, Chi-Keung},title = {Shadow Removal from Single RGB-D Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Hongteng Xu, Hongyuan Zha, Mark A. Davenport

Abstract: In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e.g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning. Decomposing a textural image as a set of patches, we achieve dynamic texture synthesis by estimating sequences of temporal patches. We select candidates for each temporal patch from spatial patches based on MRFs and regard them as samples from a low-dimensional manifold. After mapping candidates to a low-dimensional latent space, we estimate the sequence of temporal patches by finding an optimal trajectory in the latent space. Guided by some key properties of trajectories of realistic temporal patches, we derive a curvature-based trajectory selection algorithm. In contrast to the methods based on MRFs or dynamic systems that rely on a large amount of samples, our method is able to deal with the case of extremely few samples and requires no training phase. We compare our method with the state of the art and show that our method not only exhibits superior performance on synthesizing textures but it also produces results with pleasing visual effects.

@InProceedings{Xu_2014_CVPR,author = {Xu, Hongteng and Zha, Hongyuan and Davenport, Mark A.},title = {Manifold Based Dynamic Texture Synthesis from Extremely Few Samples},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Dengxin Dai, Hayko Riemenschneider, Luc Van Gool

Abstract: Example-based texture synthesis (ETS) has been widely used to generate high quality textures of desired sizes from a small example. However, not all textures are equally well reproducible that way. We predict how synthesizable a particular texture is by ETS. We introduce a dataset (21,302 textures) of which all images have been annotated in terms of their synthesizability. We design a set of texture features, such as 'textureness', homogeneity, repetitiveness, and irregularity, and train a predictor using these features on the data collection. This work is the first attempt to quantify this image property, and we find that texture synthesizability can be learned and predicted. We use this insight to trim images to parts that are more synthesizable. Also we suggest which texture synthesis method is best suited to synthesise a given texture. Our approach can be seen as 'winner-uses-all': picking one method among several alternatives, ending up with an overall superior ETS method. Such strategy could also be considered for other vision tasks: rather than building an even stronger method, choose from existing methods based on some simple preprocessing.

@InProceedings{Dai_2014_CVPR,author = {Dai, Dengxin and Riemenschneider, Hayko and Van Gool, Luc},title = {The Synthesizability of Texture Examples},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Przemyslaw Glowacki, Miguel Amavel Pinheiro, Engin Turetken, Raphael Sznitman, Daniel Lebrecht, Jan Kybic, Anthony Holtmaat, Pascal Fua

Abstract: We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches. Instead of reconstructing structures in each image independently, we do so for all images simultaneously to take advantage of temporal-consistency constraints. We show that this problem can be formulated as a Quadratic Mixed Integer Program and solved efficiently. The outcome of our approach is a framework that provides substantial improvements in reconstructions over traditional single time-instance formulations. Furthermore, an added benefit of our approach is the ability to automatically detect places where significant changes have occurred over time, which is challenging when considering large amounts of data.

@InProceedings{Glowacki_2014_CVPR,author = {Glowacki, Przemyslaw and Amavel Pinheiro, Miguel and Turetken, Engin and Sznitman, Raphael and Lebrecht, Daniel and Kybic, Jan and Holtmaat, Anthony and Fua, Pascal},title = {Reconstructing Evolving Tree Structures in Time Lapse Sequences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jingjia Xu, Azar Rahimi Dehaghani, Fei Gao, Linwei Wang

Abstract: This paper describes the development and application of a new approach to total-variation (TV) minimization for reconstruction problems on geometrically-complex and unstructured volumetric mesh. The driving application of this study is the reconstruction of 3D ischemic regions in the heart from noninvasive body-surface potential data, where the use of a TV-prior can be expected to promote the reconstruction of two piecewise smooth regions of healthy and ischemic electrical properties with localized gradient in between. Compared to TV minimization on regular grids of pixels/voxels, the complex unstructured volumetric mesh of the heart poses unique challenges including the impact of mesh resolutions on the TV-prior and the difficulty of gradient calculation. In this paper, we introduce a variational TV-prior and, when combined with the iteratively re-weighted least-square concept, a new algorithm to TV minimization that is computationally efficient and robust to the discretization resolution. In a large set of simulation studies as well as two initial real-data studies, we show that the use of the proposed  TV prior outperforms L2-based penalties in reconstruct ischemic regions, and it shows higher robustness and efficiency compared to the commonly used discrete TV prior. We also investigate the performance of the proposed TV-prior in combination with a L2- versus L1-based data fidelity term. The proposed method can extend TV-minimization to a border range of applications that involves physical domains of complex shape and unstructured volumetric mesh.

@InProceedings{Xu_2014_CVPR,author = {Xu, Jingjia and Rahimi Dehaghani, Azar and Gao, Fei and Wang, Linwei},title = {Total-Variation Minimization on Unstructured Volumetric Mesh: Biophysical Applications on Reconstruction of 3D Ischemic Myocardium},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yuanxiang Wang, Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri

Abstract: Tractography refers to the process of tracing out the nerve fiber bundles from diffusion Magnetic Resonance Images (dMRI) data acquired either in vivo or ex-vivo. Tractography is a mature research topic within the field of diffusion MRI analysis, nevertheless, several new methods are being proposed on a regular basis thereby justifying the need, as the problem is not fully solved. Tractography is usually applied to the model (used to represent the diffusion MR signal or a derived quantity) reconstructed from the acquired data. Separating shape and orientation of these models was previously shown to approximately preserve diffusion anisotropy (a useful bio-marker) in the ubiquitous problem of interpolation. However, no further intrinsic geometric properties of this framework were exploited to date in literature. In this paper, we propose a new intrinsic recursive filter on the product manifold of shape and orientation. The recursive filter, dubbed IUKFPro, is a generalization of the unscented Kalman filter (UKF) to this product manifold. The salient contributions of this work are: (1) A new intrinsic UKF for the product manifold of shape and orientation. (2) Derivation of the Riemannian geometry of the product manifold. (3) IUKFPro is tested on synthetic and real data sets from various tractography challenge competitions. From the experimental results, it is evident that IUKFPro performs better than several competing schemes in literature with regards to some of the error measures used in the competitions and is competitive with respect to others.

@InProceedings{Wang_2014_CVPR,author = {Wang, Yuanxiang and Salehian, Hesamoddin and Cheng, Guang and Vemuri, Baba C.},title = {Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Erkang Cheng, Yu Pang, Ying Zhu, Jingyi Yu, Haibin Ling

Abstract: Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field as a result of complex 3D anatomical movements projected into 2D images. To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation. Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 l1 tensor approximation. The model prior is propagated in the course of deformable tracking.  Both the higher order information and the model prior provide powerful discriminative cues for reducing ambiguity arising from the complex background, and consequently improve the tracking robustness. To validate the proposed approach, we applied it to catheter and vascular structures tracking and tested on X-ray fluoroscopic sequences obtained from 17 clinical cases. The results show, both quantitatively and qualitatively, that our approach achieves a mean tracking error of 1.4 pixels for vascular structure and 1.3 pixels for catheter tracking.

@InProceedings{Cheng_2014_CVPR,author = {Cheng, Erkang and Pang, Yu and Zhu, Ying and Yu, Jingyi and Ling, Haibin},title = {Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Christian Ledig, Wenzhe Shi, Wenjia Bai, Daniel Rueckert

Abstract: The quantification of similarity between image segmentations is a complex yet important task. The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias. Similarity measures based on overlap, e.g. Dice score, or surface distances, e.g. Hausdorff distance, clearly do not satisfy all of these properties. To address this problem, we introduce Patch-based Evaluation of Image Segmentation (PEIS), a general method to assess segmentation quality. Our method is based on finding patch correspondences and the associated patch displacements, which allow the estimation of segmentation bias. We quantify both the agreement of the segmentation boundary and the conservation of the segmentation shape. We further assess the segmentation complexity within patches to weight the contribution of local segmentation similarity to the global score. We evaluate PEIS on both synthetic data and two medical imaging datasets. On synthetic segmentations of different shapes, we provide evidence that PEIS, in comparison to the Dice score, produces more comparable scores, has increased sensitivity and estimates segmentation bias accurately. On cardiac magnetic resonance (MR) images, we demonstrate that PEIS can evaluate the performance of a segmentation method independent of the size or complexity of the segmentation under consideration. On brain MR images, we compare five different automatic hippocampus segmentation techniques using PEIS. Finally, we visualise the segmentation bias on a selection of the cases.

@InProceedings{Ledig_2014_CVPR,author = {Ledig, Christian and Shi, Wenzhe and Bai, Wenjia and Rueckert, Daniel},title = {Patch-based Evaluation of Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Scan-line optimization via cost accumulation has become very popular for stereo estimation in computer vision applications and is often combined with a semi-global cost integration strategy, known as SGM.  This paper introduces this combination as a general and effective optimization technique. It is the first time that this concept is applied to 3D medical image registration.  The presented algorithm, SGM-3D, employs a coarse-to-fine strategy and reduces the search space dimension for consecutive pyramid levels by a fixed linear rate. This allows it to handle large displacements to an extent that is required for clinical applications in high dimensional data.  SGM-3D is evaluated in context of pulmonary motion analysis on the recently extended DIR-lab benchmark that provides ten 4D computed tomography (CT) image data sets, as well as ten challenging 3D CT scan pairs from the COPDgene study archive. Results show that both registration errors as well as run-time performance are very competitive with current state-of-the-art methods.

@InProceedings{Hermann_2014_CVPR,author = {Hermann, Simon},title = {Evaluation of Scan-Line Optimization for 3D Medical Image Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yin Zhou, Hang Chang, Kenneth Barner, Paul Spellman, Bahram Parvin

Abstract: Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSDSPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]).

@InProceedings{Zhou_2014_CVPR,author = {Zhou, Yin and Chang, Hang and Barner, Kenneth and Spellman, Paul and Parvin, Bahram},title = {Classification of Histology Sections via Multispectral Convolutional Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen

Abstract: Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis  presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.

@InProceedings{Zhu_2014_CVPR,author = {Zhu, Xiaofeng and Suk, Heung-Il and Shen, Dinggang},title = {Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative method that may not necessarily perform well on classifying neuroimaging data. In this paper, we propose a learning framework to effectively improve the discriminative power of SICEs by taking advantage of the samples in the opposite class. We formulate our objective as convex optimization problems for both one-class and two-class classifications. By analyzing these optimization problems, we not only solve them efficiently in their dual form, but also gain insights into this new learning framework. The proposed framework is applied to analyzing the brain metabolic covariant networks built upon FDG-PET images for the prediction of the Alzheimer's disease, and shows significant improvement of classification performance for both one-class and two-class scenarios. Moreover, as SICE is a general method for learning undirected Gaussian graphical models, this paper has broader meanings beyond the scope of brain research.

@InProceedings{Zhou_2014_CVPR,author = {Zhou, Luping and Wang, Lei and Ogunbona, Philip},title = {Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Touseef Ahmad Qureshi, Andrew Hunter, Bashir Al-Diri

Abstract: Retinal images contain forests of mutually intersecting and overlapping venous and arterial vascular trees. The geometry of these trees shows adaptation to vascular diseases including diabetes, stroke and hypertension. Segmentation of the retinal vascular network is complicated by inconsistent vessel contrast, fuzzy edges, variable image quality, media opacities, complex intersections and overlaps. This paper presents a Bayesian approach to resolving the configuration of vascular junctions to correctly construct the vascular trees. A probabilistic model of vascular joints (terminals, bridges and bifurcations) and their configuration in junctions is built, and Maximum A Posteriori (MAP) estimation used to select most likely configurations. The model is built using a reference set of 3010 joints extracted from the DRIVE public domain vascular segmentation dataset, and evaluated on 3435 joints from the DRIVE test set, demonstrating an accuracy of 95.2%.

@InProceedings{Qureshi_2014_CVPR,author = {Ahmad Qureshi, Touseef and Hunter, Andrew and Al-Diri, Bashir},title = {A Bayesian Framework For the Local Configuration of Retinal Junctions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Gerard Sanroma, Guorong Wu, Yaozong Gao, Dinggang Shen

Abstract: Recently, multi-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption of MAS is that multiple atlases encompass richer anatomical variability than a single atlas.  Therefore, we can label the target image more accurately by mapping the label information from the appropriate atlas images that have the most similar structures. The problem of atlas selection, however, still remains unexplored. Current state-of-the-art MAS methods rely on image similarity to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to segmentation performance and, thus may undermine segmentation results. To solve this simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would eventually lead to more accurate image segmentation. Our idea is to learn the relationship between the pairwise appearance of observed instances (a pair of atlas and target images) and their final labeling performance (in terms of Dice ratio). In this way, we can select the best atlases according to their expected labeling accuracy. It is worth noting that our atlas selection method is general enough to be integrated with existing MAS methods. As is shown in the experiments, we achieve significant improvement after we integrate our method with 3 widely used MAS methods on ADNI and LONI LPBA40 datasets.

@InProceedings{Sanroma_2014_CVPR,author = {Sanroma, Gerard and Wu, Guorong and Gao, Yaozong and Shen, Dinggang},title = {Learning-Based Atlas Selection for Multiple-Atlas Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks. This recently proposed level-set formulation achieves reasonably accurate results in several segmentation problems, and has the advantage of eliminating periodic re-initializations during the optimization process, and as a result it avoids numerical errors. Nevertheless, when applied to challenging problems, such as the left ventricle segmentation from short axis cine magnetic ressonance (MR) images, the accuracy obtained by this distance regularized level set is lower than the state of the art. The main reasons behind this lower accuracy are the dependence on good initial guess for the level set optimization and on reliable appearance models. We address these two issues with an innovative structured inference using deep belief networks that produces reliable initial guess and appearance model. The effectiveness of our method is demonstrated on the MICCAI 2009 left ventricle segmentation challenge, where we show that our approach achieves one of the most competitive results (in terms of segmentation accuracy) in the field.

@InProceedings{Ngo_2014_CVPR,author = {Anh Ngo, Tuan and Carneiro, Gustavo},title = {Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-structured Inference},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition. These problems jointly determine a parameter for each of the regions in the partition. Given an estimate of the parameters, a fast approximate solution to the multi-label sub-problem is derived by a global update that uses smoothing and thresholding. The method is empirically validated to be robust to fine details of the image that plague local solutions. Further, in comparison to global methods for the multi-label problem, the method is more efficient and it is easy for a non-specialist to implement. We give sample Matlab code for the multi-label Chan-Vese problem in this paper!  Experimental comparison to the state-of-the-art in multi-label solutions to Region Competition shows that our method achieves equal or better accuracy, with the main advantage being speed and ease of implementation.

@InProceedings{Sundaramoorthi_2014_CVPR,author = {Sundaramoorthi, Ganesh and Hong, Byung-Woo},title = {FAST LABEL: Easy and Efficient Solution of Joint Multi-Label and Estimation Problems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Learning to Group Objects

Victoria Yanulevskaya, Jasper Uijlings, Nicu Sebe

Abstract: This paper presents a novel method to generate a hypothesis set of class-independent object regions. It has been shown that such object regions can be used to focus computer vision techniques on the parts of an image that matter most leading to significant improvements in both object localisation and semantic segmentation in recent years. Of course, the higher quality of class-independent object regions, the better subsequent computer vision algorithms can perform. In this paper we focus on generating higher quality object hypotheses.  We start from an oversegmentation for which we propose to extract a wide variety of region-features. We group regions together in a hierarchical fashion, for which we train a Random Forest which predicts at each stage of the hierarchy the best possible merge. Hence unlike other approaches, we use relatively powerful features and classifiers at an early stage of the generation of likely object regions. Finally, we identify and combine stable regions in order to capture objects which consist of dissimilar parts. We show on the PASCAL 2007 and 2012 datasets that our method yields higher quality regions than competing approaches while it is at the same time more computationally efficient.

@InProceedings{Yanulevskaya_2014_CVPR,author = {Yanulevskaya, Victoria and Uijlings, Jasper and Sebe, Nicu},title = {Learning to Group Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Fan Wang, Qixing Huang, Maks Ovsjanikov, Leonidas J. Guibas

Abstract: Joint segmentation of image sets is a challenging problem, especially when there are multiple objects with variable appearance shared among the images in the collection and the set of objects present in each particular image is itself varying and unknown. In this paper, we present a novel method to jointly segment a set of images containing objects from multiple classes.  We first establish consistent functional maps across the input images, and introduce a formulation that explicitly models partial similarity across images instead of global consistency. Given the optimized maps between pairs of images, multiple groups of consistent segmentation functions are found such that they align with segmentation cues in the images, agree with the functional maps, and are mutually exclusive. The proposed fully unsupervised approach exhibits a significant improvement over the state-of-the-art methods, as shown on the co-segmentation data sets MSRC, Flickr, and PASCAL.

@InProceedings{Wang_2014_CVPR,author = {Wang, Fan and Huang, Qixing and Ovsjanikov, Maks and Guibas, Leonidas J.},title = {Unsupervised Multi-Class Joint Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Semantic Object Selection

Abstract: Interactive object segmentation has great practical importance in computer vision. Many interactive methods have been proposed utilizing user input in the form of mouse clicks and mouse strokes, and often requiring a lot of user intervention. In this paper, we present a system with a far simpler input method: the user needs only give the name of the desired object.  With the tag provided by the user we do a text query of an image database to gather exemplars of the object. Using object proposals and borrowing ideas from image retrieval and object detection, the object is localized in the target image.  An appearance model generated from the exemplars and the location prior are used in an energy minimization framework to select the object. Our method outperforms the state-of-the-art on existing datasets and on a more challenging dataset we collected.

@InProceedings{Ahmed_2014_CVPR,author = {Ahmed, Ejaz and Cohen, Scott and Price, Brian},title = {Semantic Object Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The state-of-the-art in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings. Due to their impressive performance, such segmentation approaches have become building blocks in many computer vision applications. Nevertheless, the main bottlenecks are still the computationally demanding processes of local feature processing and spectral analysis. In this paper, we demonstrate that based on a discrete-continuous optimization of oriented gradient signals, we are able to provide segmentation performance competitive to state-of-the-art on BSDS 500 (even without any spectral analysis) while reducing computation time by a factor of 40 and memory demands by a factor of 10.

@InProceedings{Donoser_2014_CVPR,author = {Donoser, Michael and Schmalstieg, Dieter},title = {Discrete-Continuous Gradient Orientation Estimation for Faster Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin

Abstract: We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.

@InProceedings{Fu_2014_CVPR,author = {Fu, Huazhu and Xu, Dong and Zhang, Bao and Lin, Stephen},title = {Object-based Multiple Foreground Video Co-segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Rashmi Tonge, Subhransu Maji, C. V. Jawahar

Abstract: We propose an approach for segmenting the individual buildings in typical skyline images. Our approach is based on a Markov Random Field (MRF) formulation that exploits the fact that such images contain overlapping objects of similar shapes exhibiting a "tiered" structure. Our contributions are the following: (1) A dataset of 120 high-resolution skyline images from twelve different cities with over 4,000 individually labeled buildings that allows us to quantitatively evaluate the performance of various segmentation methods, (2) An analysis of low-level features that are useful for segmentation of buildings, and (3) A shape-constrained MRF formulation that enforces shape priors over the regions. For simple shapes such as rectangles, our formulation is significantly faster to optimize than a standard MRF approach, while also being more accurate. We experimentally evaluate various MRF formulations and demonstrate the effectiveness of our approach in segmenting skyline images.

@InProceedings{Tonge_2014_CVPR,author = {Tonge, Rashmi and Maji, Subhransu and Jawahar, C. V.},title = {Parsing World's Skylines using Shape-Constrained MRFs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.

@InProceedings{Yang_2014_CVPR,author = {Yang, Wei and Luo, Ping and Lin, Liang},title = {Clothing Co-Parsing by Joint Image Segmentation and Labeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jia Xu, Alexander G. Schwing, Raquel Urtasun

Abstract: We tackle the problem of weakly labeled semantic segmentation, where the only source of annotation are  image tags encoding  which classes are present in the  scene. This is an extremely difficult problem as no pixel-wise labelings are available, not even at training time. In this paper, we show that this problem can be formalized as an instance of  learning in a latent structured prediction framework, where the graphical model encodes the presence and absence of a class as well as the assignments of semantic labels to superpixels.  As a consequence,  we are able to leverage standard algorithms with good theoretical properties.  We demonstrate the effectiveness of our approach using the challenging SIFT-flow dataset  and show average per-class accuracy improvements  of   7%    over  the state-of-the-art.

@InProceedings{Xu_2014_CVPR,author = {Xu, Jia and Schwing, Alexander G. and Urtasun, Raquel},title = {Tell Me What You See and I will Show You Where It Is},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, Raquel Urtasun

Abstract: Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a  budget of tens or hundreds of thousands of dollars. Thus, developing solutions that can automatically perform the labeling given only weak supervision is key to reduce this cost. In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes. We formulate the problem as the one of inference in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints. We demonstrate the effectiveness of our approach in the context of autonomous driving, and show that we can segment cars with the accuracy of 86% intersection-over-union, performing  as well as  highly recommended MTurkers!

@InProceedings{Chen_2014_CVPR,author = {Chen, Liang-Chieh and Fidler, Sanja and Yuille, Alan L. and Urtasun, Raquel},title = {Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andrea Cohen, Alexander G. Schwing, Marc Pollefeys

Abstract: We propose a sequential optimization technique for segmenting a rectified image of a facade into semantic categories. Our method retrieves a parsing which respects common architectural constraints and also returns a certificate for global optimality. Contrasting the suggested method, the considered facade labeling problem is typically tackled as a classification task or as grammar parsing. Both approaches are not capable of fully exploiting the regularity of the problem.  Therefore, our technique very significantly improves the accuracy compared to the state-of-the-art while being an order of magnitude faster.  In addition, in 85% of the test images we obtain a certificate for optimality.

@InProceedings{Cohen_2014_CVPR,author = {Cohen, Andrea and Schwing, Alexander G. and Pollefeys, Marc},title = {Efficient Structured Parsing of Facades Using Dynamic Programming},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr

Abstract: The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. "I see a shiny red chair'). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.

@InProceedings{Zheng_2014_CVPR,author = {Zheng, Shuai and Cheng, Ming-Ming and Warrell, Jonathan and Sturgess, Paul and Vineet, Vibhav and Rother, Carsten and Torr, Philip H. S.},title = {Dense Semantic Image Segmentation with Objects and Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Felix Heide, Lei Xiao, Wolfgang Heidrich, Matthias B. Hullin

Abstract: The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.

@InProceedings{Heide_2014_CVPR,author = {Heide, Felix and Xiao, Lei and Heidrich, Wolfgang and Hullin, Matthias B.},title = {Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jingyu Lin, Yebin Liu, Matthias B. Hullin, Qionghai Dai

Abstract: A transient image is the optical impulse response of a scene which visualizes light propagation during an ultra-short time interval. In this paper we discover that the data captured by a multifrequency time-of-flight (ToF) camera is the Fourier transform of a transient image, and identify the sources of systematic error. Based on the discovery we propose a novel framework of frequency-domain transient imaging, as well as algorithms to remove systematic error. The whole process of our approach is of much lower computational cost, especially lower memory usage, than Heide et al.'s approach using the same device. We evaluate our approach on both synthetic and real-datasets.

@InProceedings{Lin_2014_CVPR,author = {Lin, Jingyu and Liu, Yebin and Hullin, Matthias B. and Dai, Qionghai},title = {Fourier Analysis on Transient Imaging with a Multifrequency Time-of-Flight Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chenguang Ma, Xing Lin, Jinli Suo, Qionghai Dai, Gordon Wetzstein

Abstract: Capturing and understanding visual signals is one of the core interests of computer vision. Much progress has been made w.r.t. many aspects of imaging, but the reconstruction of refractive phenomena, such as turbulence, gas and heat flows, liquids, or transparent solids, has remained a challenging problem. In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation. We show how coded illumination in combination with pairs of recorded images allow for robust computational reconstruction of dynamic two and three-dimensional refractive phenomena.

@InProceedings{Ma_2014_CVPR,author = {Ma, Chenguang and Lin, Xing and Suo, Jinli and Dai, Qionghai and Wetzstein, Gordon},title = {Transparent Object Reconstruction via Coded Transport of Intensity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Matthew O'Toole, John Mather, Kiriakos N. Kutulakos

Abstract: We consider the problem of deliberately manipulating the direct and indirect light flowing through a time-varying, fully-general scene in order to simplify its visual analysis. Our approach rests on a crucial link between stereo geometry and light transport: while direct light always obeys the epipolar geometry of a projector-camera pair, indirect light overwhelmingly does not. We show that it is possible to turn this observation into an imaging method that analyzes light transport in real time in the optical domain, prior to acquisition. This yields three key abilities that we demonstrate in an experimental camera prototype: (1) producing a live indirect-only video stream for any scene, regardless of geometric or photometric complexity; (2) capturing images that make existing structured-light shape  recovery algorithms robust to indirect transport; and (3) turning them into one-shot methods for dynamic 3D shape capture.

@InProceedings{O'Toole_2014_CVPR,author = {O'Toole, Matthew and Mather, John and Kutulakos, Kiriakos N.},title = {3D Shape and Indirect Appearance by Structured Light Transport},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Che-Han Chang, Yoichi Sato, Yung-Yu Chuang

Abstract: This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation of the overlapping regions into the non-overlapping regions and the resultant warp gradually changes from projective to similarity across the image. The proposed warp has the strengths of both projective and similarity warps. It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps. It can also be combined with more advanced local-warp-based alignment methods such as the as-projective-as-possible warp for better alignment accuracy. With the proposed warp, the field of view can be extended by stitching images with less projective distortion (stretched shapes and enlarged sizes).

@InProceedings{Chang_2014_CVPR,author = {Chang, Che-Han and Sato, Yoichi and Chuang, Yung-Yu},title = {Shape-Preserving Half-Projective Warps for Image Stitching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Parallax-tolerant Image Stitching

Abstract: Parallax handling is a challenging task for image stitching. This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching. Instead, they only need to be aligned in a way that there exists a local region where they can be seamlessly blended together. We adopt a hybrid alignment model that combines homography and content-preserving warping to provide flexibility for handling parallax and avoiding objectionable local distortion. We then develop an efficient randomized algorithm to search for a homography, which, combined with content-preserving warping, allows for optimal stitching. We predict how well a homography enables plausible stitching by finding a plausible seam and using the seam cost as the quality metric. We develop a seam finding method that estimates a plausible seam from only roughly aligned images by considering both geometric alignment and image content. We then pre-align input images using the optimal homography and further use content-preserving warping to locally refine the alignment. We finally compose aligned images together using a standard seam-cutting algorithm and a multi-band blending algorithm. Our experiments show that our method can effectively stitch images with large parallax that are difficult for existing methods.

@InProceedings{Zhang_2014_CVPR,author = {Zhang, Fan and Liu, Feng},title = {Parallax-tolerant Image Stitching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Santosh K. Divvala, Ali Farhadi, Carlos Guestrin

Abstract: Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. Our approach leverages vast resources of online books to discover the vocabulary of variance, and intertwines the data collection and modeling steps to alleviate the need for explicit human supervision in training the models. Our approach organizes the visual knowledge about a concept in a convenient and useful way, enabling a variety of applications across vision and NLP. Our online system has been queried by users to learn models for several interesting concepts including breakfast, Gandhi, beautiful, etc. To date, our system has models available for over 50,000 variations within 150 concepts, and has annotated more than 10 million images with bounding boxes.

@InProceedings{Divvala_2014_CVPR,author = {Divvala, Santosh K. and Farhadi, Ali and Guestrin, Carlos},title = {Learning Everything about Anything: Webly-Supervised Visual Concept Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1-normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. On the other hand, in the bag-of-feature (BoF) framework, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local descriptors, e.g., SIFT, and thereby we propose the method of Dirichlet-derived GMM Fisher kernel. In the experiments on diverse image classification tasks including recognition of subordinate objects and material textures, the proposed methods improve the performance of the histogram-based features and BoF-based Fisher kernel, being favorably competitive with the state-of-the-arts.

@InProceedings{Kobayashi_2014_CVPR,author = {Kobayashi, Takumi},title = {Dirichlet-based Histogram Feature Transform for Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Philip Torr

Abstract: Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure.  We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1,000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.

@InProceedings{Cheng_2014_CVPR,author = {Cheng, Ming-Ming and Zhang, Ziming and Lin, Wen-Yan and Torr, Philip},title = {BING: Binarized Normed Gradients for Objectness Estimation at 300fps},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}