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Deep Convolutional Network Cascade for Facial Point Detection

Deep Convolutional Network Cascade for Facial Point Detectio...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

We propose a new approach for estimation of the positions of facial keypoints with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy keypoints. there are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each keypoint. Second, since the networks are trained to predict all the keypoints simultaneously, the geometric constraints among keypoints are implicitly encoded. the method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. the networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the- art methods in both detection accuracy and reliability(1).

关键词： face recognition feature extraction hidden feature removal image texture pose estimation

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Sparse Quantization for Patch Description

Sparse Quantization for Patch Description

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Boix, Xavier Gygli, Michael Roig, Gemma Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9780769549897

the representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulation of patch description, that serves such issues well. Sparse quantization lies at its heart. this allows for efficient encodings, leading to powerful, novel binary descriptors, yet also to the generalization of existing descriptors like SIFT or BRIEF. We demonstrate the capabilities of our formulation for both keypoint matching and image classification. Our binary descriptors achieve state-of-the-art results for two keypoint matching benchmarks, namely those by Brown [6] and Mikolajczyk [18]. For image classification, we propose new descriptors that perform similar to SIFT on Caltech101 [10] and PASCAL VOC07 [9].

关键词： compressed sensing computer vision image classification image coding image representation

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Multi-Level Discriminative Dictionary Learning towards Hierarchical Visual Categorization

Multi-Level Discriminative Dictionary Learning towards Hiera...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Shen, Li Wang, Shuhui Sun, Gang Jiang, Shuqiang Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intell Info Proc Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9780769549897

For the task of visual categorization, the learning model is expected to be endowed with discriminative visual feature representation and flexibilities in processing many categories. Many existing approaches are designed based on a flat category structure, or rely on a set of pre-computed visual features, hence may not be appreciated for dealing with large numbers of categories. In this paper, we propose a novel dictionary learning method by taking advantage of hierarchical category correlation. For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity. Moreover, the dictionaries in lower levels also inherit the dictionary of ancestor nodes, so that categories in lower levels are described with multi-scale visual information using our dictionary learning approach. Experiments on ImageNet object data subset and SUN397 scene dataset demonstrate that our approach achieves promising performance on data with large numbers of classes compared with some state-of-the-art methods, and is more efficient in processing large numbers of categories.

关键词： dictionaries feature extraction image representation learning (artificial intelligence)

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An iterated l₁ Algorithm for Non-smooth Non-convex Optimization in computer vision

An iterated l1 Algorithm for Non-smooth No...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Ochs, Peter Dosovitskiy, Alexey Brox, thomas Pock, thomas Univ Freiburg Freiburg Germany Graz Univ Technol A-8010 Graz Austria

ISBN: (纸本)9780769549897

Natural image statistics indicate that we should use non-convex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed l(1) minimization has been proposed as a way to tackle a class of non-convex functions by solving a sequence of convex l(2)-l(1) problems. Here we extend the problem class to linearly constrained optimization of a Lipschitz continuous function, which is the sum of a convex function and a function being concave and increasing on the non-negative orthant (possibly non-convex and non-concave on the whole space). this allows to apply the algorithm to many computer vision tasks. We show the effect of non-convex regularizers on image denoising, deconvolution, optical flow, and depth map fusion. Non-convexity is particularly interesting in combination with total generalized variation and learned image priors. Efficient optimization is made possible by some important properties that are shown to hold.

关键词： computer vision concave programming convex programming deconvolution image denoising image sequences iterative methods minimisation

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Unnatural L₀ Sparse Representation for Natural Image Deblurring

Unnatural L0 Sparse Representation for Nat...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Xu, Li Zheng, Shicheng Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L-0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.

关键词： convergence of numerical methods image motion analysis image representation image restoration iterative methods optimisation sparse matrices

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Unsupervised Salience Learning for Person Re-identification

Unsupervised Salience Learning for Person Re-identification

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Rui Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

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

关键词： feature extraction image matching learning (artificial intelligence)

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Local Sparse Discriminant Analysis For Robust Face recognition

Local Sparse Discriminant Analysis For Robust Face Recogniti...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Kang, Cuicui Liao, Shengcai Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9780769549903

the Linear Discriminant Analysis (LDA) algorithm plays an important role in pattern recognition. A common practice is that LDA and many of its variants generally learn dense bases, which are not robust to local image distortions and partial occlusions. Recently, the LASSO penalty has been incorporated into LDA to learn sparse bases. However, since the learned sparse coefficients are globally distributed all over the basis image, the solution is still not robust to partial occlusions. In this paper, we propose a Local Sparse Discriminant Analysis (LoSDA) method, which aims at learning discriminant bases that consist of local object parts. In this way, it is more robust than dense or global basis based LDA algorithms for visual classification. the proposed model is formulated as a constrained least square regression problem with a group sparse regularization. Furthermore, we derive a weighted LoSDA (WLoS-DA) approach to learn localized basis images, which also enables multi subspace learning and fusion. Finally, we develop an algorithm based on the Accelerated Proximal Gradient (APG) technique to solve the resulting weighted group sparse optimization problem. Experimental results on the FRGC v2.0 and the AR face databases show that the proposed LoSDA and WLoSDA algorithms both outperform the other state-of-the-art discriminant subspace learning algorithms under illumination variations and occlusions.

关键词： computer vision face recognition gradient methods image classification image fusion learning (artificial intelligence) least squares approximations optimisation regression analysis

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Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos

Scale and Rotation Invariant Approach to Tracking Human Body...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Bo, Yihang Jiang, Hao Chinese Acad Sci Inst Automat Beijing Peoples R China Boston Coll Dept Comp Sci Chestnut Hill MA 02167 USA

ISBN: (纸本)9780769549903

We propose a novel scale and rotation invariant method to track a human subject's body part regions in cluttered videos. the proposed method optimizes the assembly of body part region proposals with the spatial and temporal constraints of a human body plan. this approach is invariant to the object scale and rotation changes. To enable scale and rotation invariance, the human body part graph of the proposed method has to be loopy;efficiently optimizing the body part region assembly is a great challenge. We propose a dynamic programming method to solve the problem. We devise a method that finds N-best whole body configurations from loopy structures in each video frame using dynamic programming. the N-best configurations are then used to construct trellises with which we track human body part regions by finding shortest paths on the trellises. Our experiments on a variety of videos show that the proposed method is efficient, accurate and robust against object appearance variations, scale and rotation changes and background clutter.

关键词： dynamic programming graph theory object tracking video signal processing

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Integrating the image identifiable principle of human cognition and computer vision to develop a new pattern recognition design system for smart home

Integrating the image identifiable principle of human cognit...

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7th International conference on Universal Access in Human-computer Interaction: Design Methods, Tools, and Interaction Techniques for eInclusion, UAHCI 2013, Held as Part of 15th International conference on Human-computer Interaction, HCI 2013

作者： Wang, Pin-Chin Tseng, Wan-Ting Cheng, Chun-Min Sung, Yi-Hsuan Chou, Yi-Chun Wu, Fong-Gong Department of Industrial Design National Cheng Kung University Tainan 70101 Taiwan

ISBN: (纸本)9783642391903

In this study, we invented a new way which classifies objects according to their functions and the regions of use. then we proceeded to innovate and design the systematic pattern on the objects. For this goal, we make the pattern on the objects as the goal of recognition that can be captured by camera. In this study we intend to design a new way to derive pattern reasonably and that can be recognized by algorithms. the result of our pattern recognition program test pattern that consisted of systematic components showed that the identification rate of success was more than 90%. this result indicated that our pattern design method can derive a significant pattern which is recognized by computer algorithms and can be effective in detecting the state of the object in using. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Software testing

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Multi-target Tracking by Rank-1 Tensor Approximation

Multi-target Tracking by Rank-1 Tensor Approximation

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Shi, Xinchu Ling, Haibin Xing, Junliang Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9780769549897

In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an l(1) norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory candidate. the local assignment variables are the l(1) normalized vectors, which are used to approximate the rank-1 tensor. Our approach provides a flexible and effective formulation where both pairwise and high-order association energies can be used expediently. We also show the close relation between our formulation and the multi-dimensional assignment (MDA) model. To solve the optimization in the rank-1 tensor approximation, we propose an algorithm that iteratively powers the intermediate solution followed by an l(1) normalization. Aside from effectively capturing high-order motion information, the proposed solver runs efficiently with proved convergence. the experimental validations are conducted on two challenging datasets and our method demonstrates promising performances on both.

关键词： approximation theory computer vision human computer interaction iterative methods target tracking tensors vectors

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