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

共 1569 条记录，以下是71-80 订阅

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Bilinear Programming for Human Activity recognition with Unknown MRF Graphs

Bilinear Programming for Human Activity Recognition with Unk...

引用

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

作者： Wang, Zhenhua Shi, Qinfeng Shen, Chunhua van den Hengel, Anton Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9780769549897

Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty. However, the underlying graph structure is often manually specified, or automatically constructed by heuristics. We show, instead, that learning an MRF graph and performing MAP inference can be achieved simultaneously by solving a bilinear program. Equipped with the bilinear program based MAP inference for an unknown graph, we show how to estimate parameters efficiently and effectively with a latent structural SVM. We apply our techniques to predict sport moves (such as serve, volley in tennis) and human activity in TV episodes (such as kiss, hug and Hi-Five). Experimental results show the proposed method outperforms the state-of-the-art.

关键词： Markov processes entertainment graph theory inference mechanisms learning (artificial intelligence) support vector machines

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Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots

Watching Unlabeled Video Helps Learn New Human Actions from ...

引用

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

作者： Chen, Chao-Yeh Grauman, Kristen Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9780769549897

We propose an approach to learn action categories from static images that leverages prior observations of generic human motion to augment its training process. Using unlabeled video containing various human activities, the system first learns how body pose tends to change locally in time. then, given a small number of labeled static images, it uses that model to extrapolate beyond the given exemplars and generate "synthetic" training examples-poses that could link the observed images and/or immediately precede or follow them in time. In this way, we expand the training set without requiring additional manually labeled examples. We explore both example-based and manifold-based methods to implement our idea. Applying our approach to recognize actions in both images and video, we show it enhances a state-of-the-art technique when very few labeled training examples are available.

关键词： computer based training image motion analysis video signal processing

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Poselet Key-framing: A Model for Human Activity recognition

Poselet Key-framing: A Model for Human Activity Recognition

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

作者： Raptis, Michalis Sigal, Leonid Disney Res Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative keyframes - collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of keyframes in a max-margin discriminative framework, where we treat keyframes as latent variables. this allows us to (jointly) learn a set of most discriminative keyframes while also learning the local temporal context between them. Keyframes are encoded using a spatially-localizable poselet-like representation with HoG and BoW components learned from weak annotations;we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. this results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting.

关键词： image representation image sequences pose estimation support vector machines

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Kernel Learning for Extrinsic Classification of Manifold Features

Kernel Learning for Extrinsic Classification of Manifold Fea...

引用

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

作者： Vemulapalli, Raviteja Pillai, Jaishanker K. Chellappa, Rama Univ Maryland Dept Elect & Comp Engn Ctr Automat Res UMIACS College Pk MD 20742 USA

ISBN: (纸本)9780769549897

In computer vision applications, features often lie on Riemannian manifolds with known geometry. Popular learning algorithms such as discriminant analysis, partial least squares, support vector machines, etc., are not directly applicable to such features due to the non-Euclidean nature of the underlying spaces. Hence, classification is often performed in an extrinsic manner by mapping the manifolds to Euclidean spaces using kernels. However, for kernel based approaches, poor choice of kernel often results in reduced performance. In this paper, we address the issue of kernel-selection for the classification of features that lie on Riemannian manifolds using the kernel learning approach. We propose two criteria for jointly learning the kernel and the classifier using a single optimization problem. Specifically, for the SVM classifier, we formulate the problem of learning a good kernel-classifier combination as a convex optimization problem and solve it efficiently following the multiple kernel learning approach. Experimental results on image set-based classification and activity recognition clearly demonstrate the superiority of the proposed approach over existing methods for classification of manifold features.

关键词： computer vision geometry image classification learning (artificial intelligence) optimisation support vector machines

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Enriching Texture Analysis with Semantic Data

Enriching Texture Analysis with Semantic Data

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

作者： Matthews, Tim Nixon, Mark S. Niranjan, Mahesan Univ Southampton Sch Elect & Comp Sci Commun Signal Proc & Control Grp Southampton SO9 5NH Hants England

ISBN: (纸本)9780769549897

We argue for the importance of explicit semantic modelling in human-centred texture analysis tasks such as retrieval, annotation, synthesis, and zero-shot learning. To this end, low-level attributes are selected and used to define a semantic space for texture. 319 texture classes varying in illumination and rotation are positioned within this semantic space using a pairwise relative comparison procedure. Low-level visual features used by existing texture descriptors are then assessed in terms of their correspondence to the semantic space. Textures with strong presence of attributes connoting randomness and complexity are shown to be poorly modelled by existing descriptors. In a retrieval experiment semantic descriptors are shown to outperform visual descriptors. Semantic modelling of texture is thus shown to provide considerable value in both feature selection and in analysis tasks.

关键词： feature extraction image classification image retrieval image texture learning (artificial intelligence) object recognition

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Ensemble Learning for Confidence Measures in Stereo vision

Ensemble Learning for Confidence Measures in Stereo Vision

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

作者： Haeusler, Ralf Nair, Rahul Kondermann, Daniel Univ Auckland Dept Comp Sci Auckland 1 New Zealand Heidelberg Univ Heidelberg Collaboratory Image Proc D-69115 Heidelberg Germany

ISBN: (纸本)9780769549897

With the aim to improve accuracy of stereo confidence measures, we apply the random decision forest framework to a large set of diverse stereo confidence measures. Learning and testing sets were drawn from the recently introduced KITTI dataset, which currently poses higher challenges to stereo solvers than other benchmarks with ground truth for stereo evaluation. We experiment with semi global matching stereo (SGM) and a census dataterm, which is the best performing real-time capable stereo method known to date. On KITTI images, SGM still produces a significant amount of error. We obtain consistently improved area under curve values of sparsification measures in comparison to best performing single stereo confidence measures where numbers of stereo errors are large. More specifically, our method performs best in all but one out of 194 frames of the KITTI dataset.

关键词： decision trees image matching learning (artificial intelligence) stereo image processing

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Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines

Facial Feature Tracking under Varying Facial Expressions and...

引用

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

作者： Wu, Yue Wang, Zuoguan Ji, Qiang Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA

ISBN: (纸本)9780769549897

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

关键词： Boltzmann machines belief networks computer vision face recognition object tracking pose estimation solid modelling

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Graph Matching with Anchor Nodes: A Learning Approach

Graph Matching with Anchor Nodes: A Learning Approach

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

作者： Hu, Nan Rustamov, Raif M. Guibas, Leonidas Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9780769549897

In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes. Our construction exploits recently introduced node signatures based on graph Laplacians, namely the Laplacian family signature (LFS) on the nodes, and the pairwise heat kernel map on the edges. In this paper, without assuming an explicit form of parametric dependence nor a distance metric between node signatures, we formulate an optimization problem which incorporates the knowledge of anchor nodes. Solving this problem gives us an optimized proximity measure specific to the graphs under consideration. Using this as a first order compatibility term, we then set up an integer quadratic program (IQP) to solve for a near optimal graph matching. Our experiments demonstrate the superior performance of our approach on randomly generated graphs and on two widely-used image sequences, when compared with other existing signature and adjacency matrix based graph matching methods.

关键词： graph theory image sequences integer programming pattern matching quadratic programming

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Intrinsic Scene Properties from a Single RGB-D Image

Intrinsic Scene Properties from a Single RGB-D Image

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

作者： Barron, Jonathan T. Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9780769549897

In this paper we extend the "shape, illumination and reflectance from shading" (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a "soft" segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. the output of our model can be used for graphics applications, or for any application involving RGB-D images.

关键词： image segmentation object detection shape recognition

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A robust identification approach to gait recognition

A robust identification approach to gait recognition

引用

26th ieee conference on computer vision and pattern recognition, cvpr

作者： Tao, Ding Pennsylvania State University University Park PA 16802 United States

ISBN: (纸本)9781424422432

In this paper we address the problem of human gait recognition from a robust identification and model (in)validation prospective. the main idea is to apply dimensionality reduction technique to extract the spatio-temporal information by mapping the gait silhouette sequence to a low dimensional time sequence, which is considered as the output of a linear time invariant (LTI) system. A class of gaits is associated to a nominal discrete LTI system which has a periodic impulse response and is identified by robust identification approach. Correspondingly, gait recognition can be formulated as measuring the difference between the models representing different gait sequences. Our approach provides an efficient way to extract, to model shape-motion information of gait sequence, and to measure the difference between gait sequence models which is robust to gait cycle localization, gross appearance variation, and time scaling. these results are illustrated with practical examples on popular gait databases. ©2008 ieee.

关键词： Impulse response

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