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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是151-160 订阅

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Fusing Spatiotemporal Features and Joints for 3D Action recognition

Fusing Spatiotemporal Features and Joints for 3D Action Reco...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Yu Chen, Wenbin Guo, Guodong W Virginia Univ Dept CSEE Morgantown WV 26506 USA

ISBN: (纸本)9780769549903

We present a novel approach to 3D human action recognition based on a feature-level fusion of spatiotemporal features and skeleton joints. First, 3D interest points detection and local feature description are performed to extract spatiotemporal motion information. then the frame difference and pairwise distances of skeleton joint positions are computed to characterize the spatial information of the joints in 3D space. these two features are complementary to each other. A fusion scheme is then proposed to combine them effectively based on the random forests method. the proposed approach is validated on three challenging 3D action datasets for human action recognition. Experimental results show that the proposed approach outperforms the state-of-the-art methods on all three datasets.

关键词： feature extraction gesture recognition image fusion image motion analysis image thinning stereo image processing trees (mathematics)

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Selective Transfer Machine for Personalized Facial Action Unit Detection

Selective Transfer Machine for Personalized Facial Action Un...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chu, Wen-Sheng De la Torre, Fernando Cohn, Jeffery F. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. they neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. the alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all.

关键词： emotion recognition face recognition image classification learning (artificial intelligence)

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Pixel-level Hand Detection in Ego-Centric Videos

Pixel-level Hand Detection in Ego-Centric Videos

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Cheng Kitani, Kris M. Tsinghua Univ Beijing 100084 Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

We address the task of pixel-level hand detection in the context of ego-centric cameras. Extracting hand regions in ego-centric videos is a critical step for understanding hand-object manipulation and analyzing hand-eye coordination. However, in contrast to traditional applications of hand detection, such as gesture interfaces or sign-language recognition, ego-centric videos present new challenges such as rapid changes in illuminations, significant camera motion and complex hand-object manipulations. To quantify the challenges and performance in this new domain, we present a fully labeled indoor/outdoor ego-centric hand detection benchmark dataset containing over 200 million labeled pixels, which contains hand images taken under various illumination conditions. Using both our dataset and a publicly available ego-centric indoors dataset, we give extensive analysis of detection performance using a wide range of local appearance features. Our analysis highlights the effectiveness of sparse features and the importance of modeling global illumination. We propose a modeling strategy based on our findings and show that our model outperforms several baseline approaches.

关键词： feature extraction image motion analysis image resolution image sensors lighting object detection sign language recognition video signal processing

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Recognizing Actions from Depth Cameras as Weakly Aligned Multi-Part Bag-of-Poses

Recognizing Actions from Depth Cameras as Weakly Aligned Mul...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Seidenari, Lorenzo Varano, Vincenzo Berretti, Stefano Del Bimbo, Alberto Pala, Pietro Univ Florence I-50121 Florence Italy

ISBN: (纸本)9780769549903

Recently released depth cameras provide effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this work, we propose an efficient yet effective method to recognize human actions based on the positions of joints. First, the body skeleton is decomposed in a set of kinematic chains, and the position of each joint is expressed in a locally defined reference system which makes the coordinates invariant to body translations and rotations. A multi-part bag-of-poses approach is then defined, which permits the separate alignment of body parts through a nearest-neighbor classification. Experiments conducted on the Florence 3D Action dataset and the MSR Daily Activity dataset show promising results.

关键词： cameras gesture recognition image representation pattern classification position measurement

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Learning to Detect Partially Overlapping Instances

Learning to Detect Partially Overlapping Instances

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Arteta, Carlos Lempitsky, Victor Noble, J. Alison Zisserman, Andrew Univ Oxford Dept Engn Sci Oxford OX1 2JD England Skolkovo Inst Sci & Technol Moskovsky Russia

ISBN: (纸本)9780769549897

the objective of this work is to detect all instances of a class (such as cells or people) in an image. the instances may be partially overlapping and clustered, and hence quite challenging for traditional detectors, which aim at localizing individual instances. Our approach is to propose a set of candidate regions, and then select regions based on optimizing a global classification score, subject to the constraint that the selected regions are non-overlapping. Our novel contribution is to extend standard object detection by introducing separate classes for tuples of objects into the detection process. For example, our detector can pick a region containing two or three object instances, while assigning such region an appropriate label. We show that this formulation can be learned within the structured output SVM framework, and that the inference in such model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations - a dot on each instance. the improvement resulting from the addition of the capability to detect tuples of objects is demonstrated on quite disparate data sets: fluorescence microscopy images and UCSD pedestrians.

关键词： dynamic programming image classification inference mechanisms object detection pattern clustering support vector machines trees (mathematics)

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Evaluating new variants of Motion Interchange patterns

Evaluating new variants of Motion Interchange Patterns

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hanani, Yair Levy, Noga Wolf, Lior Tel Aviv Univ IL-69978 Tel Aviv Israel

ISBN: (纸本)9780769549903

Action recognition in videos is an active research field that is fueled by an acute need, spanning several application domains. Still, existing systems fall short of the applications' needs in real-world scenarios, where the quality of the video is less than optimal and the viewpoint is uncontrolled and often not static. In this paper, we extend the Motion Interchange patterns (MIP) framework for action recognition. this effective framework encodes motion by capturing local changes in motion directions and additionally uses mechanisms to suppress static edges and compensate for global camera motion. Here, we suggest to apply the MIP encoding on gradient-based descriptors to enhance invariance to light changes and achieve a better description of the motion's structure. We compare our method using patterns of Oriented Edge Magnitudes (POEM) and Difference of Gaussians (DoG) as gradient-based descriptors to the original MIP on two challenging large-scale datasets.

关键词： image coding image motion analysis image recognition

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Explicit Occlusion Modeling for 3D Object Class Representations

Explicit Occlusion Modeling for 3D Object Class Representati...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zia, M. Zeeshan Stark, Michael Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9780769549897

Despite the success of current state-of-the-art object class detectors, severe occlusion remains a major challenge. this is particularly true for more geometrically expressive 3D object class representations. While these representations have attracted renewed interest for precise object pose estimation, the focus has mostly been on rather clean datasets, where occlusion is not an issue. In this paper, we tackle the challenge of modeling occlusion in the context of a 3D geometric object class model that is capable of fine-grained, part-level 3D object reconstruction. Following the intuition that 3D modeling should facilitate occlusion reasoning, we design an explicit representation of likely geometric occlusion patterns. Robustness is achieved by pooling image evidence from of a set of fixed part detectors as well as a non-parametric representation of part configurations in the spirit of poselets. We confirm the potential of our method on cars in a newly collected data set of inner-city street scenes with varying levels of occlusion, and demonstrate superior performance in occlusion estimation and part localization, compared to baselines that are unaware of occlusions.

关键词： computer graphics image reconstruction image representation object detection pose estimation

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Weakly-Supervised Dual Clustering for Image Semantic Segmentation

Weakly-Supervised Dual Clustering for Image Semantic Segment...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yang Liu, Jing Li, Zechao Tang, Jinhui Lu, Hanqing Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Nanjing Univ Sci & Tech Sch Comp Sci Beijing 210044 Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. the proposed approach is motivated from the observation that superpixels belonging to an object class usually exist across multiple images and hence can be gathered via the idea of clustering. In WSDC, spectral clustering is adopted to cluster the superpixels obtained from a set of over-segmented images. At the same time, a linear transformation between features and labels as a kind of discriminative clustering is learned to select the discriminative features among different classes. the both clustering outputs should be consistent as much as possible. Besides, weakly-supervised constraints from image-level labels are imposed to restrict the labeling of superpixels. Finally, the non-convex and non-smooth objective function are efficiently optimized using an iterative CCCP procedure. Extensive experiments conducted on MSRC and LabelMe datasets demonstrate the encouraging performance of our method in comparison with some state-of-the-arts.

关键词： image segmentation pattern clustering

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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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A Bayesian Approach to Multimodal Visual Dictionary Learning

A Bayesian Approach to Multimodal Visual Dictionary Learning

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Irie, Go Liu, Dong Li, Zhenguo Chang, Shih-Fu NTT Corp Atsugi Kanagawa Japan Columbia Univ New York NY 10027 USA

ISBN: (纸本)9780769549897

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

关键词： Internet belief networks image classification image retrieval learning (artificial intelligence) pattern clustering text analysis

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