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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1221-1230 订阅

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Pose primitive based human action recognition in videos or still images

Pose primitive based human action recognition in videos or s...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Christian thurau Vaclav Hlavac Department of Computer Science Technical University Dortmund Germany Center for Machine Perception Czech Technical University Czech Republic

this paper presents a method for recognizing human actions based on pose primitives. In learning mode, the parameters representing poses and activities are estimated from videos. In run mode, the method can be used both for videos or still images. For recognizing pose primitives, we extend a Histogram of Oriented Gradient (HOG) based descriptor to better cope with articulated poses and cluttered background. Action classes are represented by histograms of poses primitives. For sequences, we incorporate the local temporal context by means of n-gram expressions. Action recognition is based on a simple histogram comparison. Unlike the mainstream video surveillance approaches, the proposed method does not rely on background subtraction or dynamic features and thus allows for action recognition in still images.

关键词： Humans Image recognition Videos Histograms Image sequences Target recognition Legged locomotion Shape Detectors Principal component analysis

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Detection with Multi-exit Asymmetric Boosting

Detection with Multi-exit Asymmetric Boosting

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Minh-Tri Pham Viet-Dung D. Hoang Tat-Jen Cham School of Computer Engineering Nanyang Technological University Singapore

We introduce a generalized representation for a boosted classifier with multiple exit nodes, and propose a method to training which combines the idea of propagating scores across boosted classifiers [14, 17] and the use of asymmetric goals [13]. A means for determining the ideal constant asymmetric goal is provided, which is theoretically justified under a conservative bound on the ROC operating point target and empirically near-optimal under the exact bound. Moreover, our method automatically minimizes the number of weak classifiers, avoiding the need to retrain a boosted classifier multiple times for empirical best performance as in conventional methods. Experimental results shows significant reduction in training time and number of weak classifiers, as well as better accuracy, compared to conventional cascades and multi-exit boosted classifiers.

关键词： Boosting Face detection Event detection Design methodology

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the Kneed Walker for Human Pose Tracking

The Kneed Walker for Human Pose Tracking

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Marcus A. Brubaker David J. Fleet Department of Computer Science University of Toronto Canada

the Kneed Walker is a physics-based model derived from a planar biomechanical characterization of human locomotion. By controlling torques at the knees, hips and torso, the model captures a full range of walking motions with foot contact and balance. Constraints are used to properly handle ground collisions and joint limits. A prior density over walking motions is based on dynamics that are optimized for efficient cyclic gaits over a wide range of natural human walking speeds and step lengths, on different slopes. the generative model used for monocular tracking comprises the Kneed Walker prior, a 3D kinematic model constrained to be consistent with the underlying dynamics, and a simple measurement model in terms of appearance and optical flow. the tracker is applied to people walking with varying speeds, on hills, and with occlusion.

关键词： Humans Legged locomotion Motion control Torque control Knee Hip Torso Foot Kinematics Fluid flow measurement

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Improving Local Learning for Object Categorization by Exploring the Effects of Ranking

Improving Local Learning for Object Categorization by Explor...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Tien-Lung Chang Tyng-Luh Liu Jen-Hui Chuang Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning procedure. In this work, we describe a novel method to rank neighbors by learning a local distance function, and meanwhile to derive the local distance function by focusing on the high-ranked neighbors. the two aspects of considerations can be elegantly coupled through a well-defined objective function, motivated by a supervised ranking method called P-Norm Push. While the local distance functions are learned independently, they can be reshaped altogether so that their values can be directly compared. We apply the proposed method to the Caltech-101 dataset, and demonstrate the use of proper neighbors can improve the performance of classification techniques based on nearest-neighbor selection.

关键词： Nearest neighbor searches Information science computer science Supervised learning Application software computer vision Neural networks Organizing Training data Euclidean distance

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Face Alignment via Boosted Ranking Model

Face Alignment via Boosted Ranking Model

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Hao Wu Xiaoming Liu Gianfranco Doretto Center for Automation Research University of Maryland College Park MD USA Visualization and Computer Vision Laboratory GE Global Research Center Niskayuna NY USA

Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is aligned by maximizing a score function, which we learn from training data, and that we impose to be concave. We show that this problem can be reduced to learning a classifier that is able to say whether or not by switching from one alignment to a new one, the model is approaching the correct fitting. this relates to the ranking problem where a number of instances need to be ordered. For training the model, we propose to extend GentleBoost [23] to rank-learning. Extensive experimentation shows the superiority of this approach to other learning paradigms, and demonstrates that this model exceeds the alignment performance of the state-of-the-art.

关键词： Active appearance model Deformable models Face detection Magnesium compounds Active shape model Automation Educational institutions Visualization computer vision Cost function

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Action recognition Using Ballistic Dynamics

Action Recognition Using Ballistic Dynamics

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Shiv N. Vitaladevuni Vili Kellokumpu Larry S. Davis Janelia Farm Research Campus Howard Hughes Medical Institute Ashburn VA USA Machine Vision Group University of Oulu Oulu Finland Computer Vision Laboratory University of Maryland College Park MD USA

We present a Bayesian framework for action recognition through ballistic dynamics. Psycho-kinesiological studies indicate that ballistic movements form the natural units for human movement planning. the framework leads to an efficient and robust algorithm for temporally segmenting videos into atomic movements. Individual movements are annotated with person-centric morphological labels called ballistic verbs. this is tested on a dataset of interactive movements, achieving high recognition rates. the approach is also applied on a gesture recognition task, improving a previously reported recognition rate from 84% to 92%. Consideration of ballistic dynamics enhances the performance of the popular Motion History Image feature. We also illustrate the approach's general utility on real-world videos. Experiments indicate that the method is robust to view, style and appearance variations.

关键词： Psychology Videos Image segmentation Humans Bayesian methods Robustness History Propulsion Cameras Military computing

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A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets

A Multi-Compartment Segmentation Framework With Homeomorphic...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Xian Fan Pierre-Louis Bazin Jerry L. Prince Johns Hopkins University Baltimore MD USA

the simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed;however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. the computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. the properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real.

关键词： Level set Image segmentation Topology computer vision Application software Deformable models Computational complexity Anatomy Biomedical imaging Geometry

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Observe-and-Explain: A New Approach for Multiple Hypotheses Tracking of Humans and Objects

Observe-and-Explain: A New Approach for Multiple Hypotheses ...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： M. S. Ryoo J. K. Aggarwal Computer & Vision Research Center / Department of ECE University of Technology Austin USA

this paper presents a novel approach for tracking humans and objects under severe occlusion. We introduce a new paradigm for multiple hypotheses tracking, observe-and-explain, as opposed to the previous paradigm of hypothesize-and-test. Our approach efficiently enumerates multiple possibilities of tracking by generating several likely 'explanations' after concatenating a sufficient amount of observations. the computational advantages of our approach over the previous paradigm under severe occlusions are presented. the tracking system is implemented and tested using the i-Lids dataset, which consists of videos of humans and objects moving in a London subway station. the experimental results show that our new approach is able to track humans and objects accurately and reliably even when they are completely occluded, illustrating its advantage over previous approaches.

关键词： Humans Layout Trajectory Target tracking Bayesian methods computer vision Videos computerized monitoring System testing Application software

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Learning Patch Correspondences for Improved Viewpoint Invariant Face recognition

Learning Patch Correspondences for Improved Viewpoint Invari...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Ahmed Bilal Ashraf Simon Lucey Tsuhan Chen Carnegie Mellon University USA

Variation due to viewpoint is one of the key challenges that stand in the way of a complete solution to the face recognition problem. It is easy to note that local regions of the face change differently in appearance as the viewpoint varies. Recently, patch-based approaches, such as those of Kanade and Yamada, have taken advantage of this effect resulting in improved viewpoint invariant face recognition. In this paper we propose a data-driven extension to their approach, in which we not only model how a face patch varies in appearance, but also how it deforms spatially as the viewpoint varies. We propose a novel alignment strategy which we refer to as "stackflow" that discovers viewpoint induced spatial deformities undergone by a face at the patch level. One can then view the spatial deformation of a patch as the correspondence of that patch between two viewpoints. We present improved identification and verification results to demonstrate the utility of our technique.

关键词： Face recognition Probes Humans Deformable models Lighting Image recognition Head Geometry Nose Power system modeling

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Misalignment-Robust Face recognition

Misalignment-Robust Face Recognition

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Huan Wang Shuicheng Yan thomas Huang Jianzhuang Liu Xiaoou Tang IE Chinese University of Hong Kong Hong Kong China National University of Singapore Singapore University of Illinois Urbana-Champaign USA Microsoft Research Asia Beijing China

In this paper, we study the problem of subspace-based face recognition under scenarios with spatial misalignments and/or image occlusions. For a given subspace, the embedding of a new datum and the underlying spatial misalignment parameters are simultaneously inferred by solving a constrained l{sub}1 norm optimization problem, which minimizes the error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and image occlusions all validate the effectiveness of our proposed general formulation.

关键词： Face recognition Face detection Linear discriminant analysis Constraint optimization Subspace constraints Image reconstruction Probes Principal component analysis Degradation Robustness

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