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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是4581-4590 订阅

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A hierarchical model of shape and appearance for human action classification

A hierarchical model of shape and appearance for human actio...

引用

2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Niebles, Juan Carlos Li, Fei-Fei Universidad Del Norte Colombia University of Illinois Urbana-Champaign United States Princeton University United States

ISBN: (纸本)1424411807

We present a novel model for human action categorization. A video sequence is represented as a collection of spatial and spatial-temporal features by extracting static and dynamic interest points. We propose a hierarchical model that can be characterized as a constellation of bags-of-features and that is able to combine both spatial and spatial-temporal features. Given a novel video sequence, the model is able to categorize human actions in a frame-by-frame basis. We test the model on a publicly available human action dataset [2] and show that our new method performs well on the classification task. We also conducted control experiments to show that the use of the proposed mixture of hierarchical models improves the classification performance over bag of feature models. An additional experiment shows that using both dynamic and static features provides a richer representation of human actions when compared to the use of a single feature type, as demonstrated by our evaluation in the classification task. © 2007 ieee.

关键词： pattern recognition

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Influence of numerical conditioning on the accuracy of relative orientation

Influence of numerical conditioning on the accuracy of relat...

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2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Šegvić, Siniša Schweighofer, Gerald Pinz, Axel Institute of Electrical Measurement and Measurement Signal Processing Graz University of Technology Kopernikusgasse 24/IV 8010 Graz Austria

ISBN: (纸本)1424411807

We study the influence of numerical conditioning on the accuracy of two closed-form solutions to the overconstrained relative orientation problem. We consider the well known eight-point algorithm and the recent five-point algorithm, and evaluate changes in their performance due to Hartley's normalization andMuehlich's equilibration. The need for numerical conditioning is introduced by explaining the known occurence of the bias of the eight-point algorithm towards the forward motion. Then it is shown how conditioning can be used to improve the results of the recent five-point algorithm. This is not straightforward since the conditioning disturbs the calibration of the input data. The conditioning therefore needs to be reverted before enforcing the internal cubic constraints of the essential matrix. The obtained improvements are less dramatic than in the case of the eight-point algorithm, for which we offer a plausible explanation. The theoretical claims are backed up with extensive experimentation on noisy artificial datasets, under a variety of geometric and imaging parameters. © 2007 ieee.

关键词： computer vision

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Hidden Markov models with kernel density estimation of emission probabilities and their use in activity recognition

Hidden Markov models with kernel density estimation of emiss...

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2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Piccardi, Massimo Pérez, Óscar Faculty of Information Technology University of Technology Sydney Computer Sciencie Department GIAA Universidad Carlos III de Madrid Colmenarejo Spain

ISBN: (纸本)1424411807

In this paper, we present a modified hidden Markov model with emission probabilities modelled by kernel density estimation and its use for activity recognition in videos. In the proposed approach, kernel density estimation of the emission probabilities is operated simultaneously with that of all the other model parameters by an adapted Baum-Welch algorithm. This allows us to retain maximum-likelihood estimation while overcoming the known limitations of mixture of Gaussions in modelling certain probability distributions. Experiments on activity recognition have been performed on groundtruthed data from the CAVIAR video surveillance database and reported in the paper. The error on the training and validation sets with kernel density estimation remains around 14-16% while for the conventional Gaussian mixture approach varies between 15 and 24%, strongly depending on the initial values chosen for the parameters. Overall, kernel density estimation proves capable of providing more flexible modelling of the emission probabilities and, unlike Gaussian mixtures, does not suffer from being highly parametric and of difficult initialisation. © 2007 ieee.

关键词： Hidden Markov models

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OPTIMOL: Automatic online picture collecTion via incremental MOdel learning

OPTIMOL: Automatic online picture collecTion via incremental...

引用

2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Li, Li-Jia Wang, Gang Li, Fei-Fei Dept. of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Dept. of Computer Science Princeton University United States

ISBN: (纸本)1424411807

A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-art algorithms. Dataset collection is, however, a tedious and time-consuming task. This paper presents a novel automatic dataset collecting and model learning approach that uses object recognition techniques in an incremental method. The goal of this work is to use the tremendous resources of the web to learn robust object category models in order to detect and search for objects in real-world cluttered scenes. It mimics the human learning process of iteratively accumulating model knowledge and image examples. We adapt a non-parametric graphical model and propose an incremental learning framework. Our algorithm is capable of automatically collecting much larger object category dataseis for 22 randomly selected classes from the Caltech 101 dataset. Furthermore, we offer not only more images in each object category dataset, but also a robust object model and meaningful image annotation. Our experiments show that OPTIMOL is capable of collecting image datasets that are superior to Caltech 101 and LabelMe. © 2007 ieee.

关键词： Learning systems

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Graphical model approach to iris matching under deformation and occlusion

Graphical model approach to iris matching under deformation ...

引用

2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Kerekes, R. Narayanaswamy, B. Thornton, J. Savvides, M. Vijaya Kumar, B.V.K. ECE Department Carnegie Mellon University Pittsburgh PA 15213

ISBN: (纸本)1424411807

Template matching of iris images for biométrie recognition typically suffers from both local deformations between the template and query images and large occlusions from the eyelid. In this work, we model deformation and occlusion as a set of hidden variables for each iris comparison. We use a field of directional vectors to represent deformation and a field of binary variables to represent occlusion. We impose a probability distribution on these fields using a lattice-type undirected graphical model, in which the graph edges represent interdependencies between neighboring iris regions. Gabor wavelet-based similarity scores and intensity statistics are used as observations in the model. Loopy belief propagation is applied to estimate the conditional distributions on the hidden variables, which are in turn used to compute final match scores. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the Iris Challenge Evaluation (ICE). We show that our proposed method significantly improves recognition accuracy on these datasets over existing methods. © 2007 ieee.

关键词： computer graphics

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Large scale vision-based navigation without an accurate global reconstruction

Large scale vision-based navigation without an accurate glob...

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2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Šegvić, Siniša Remazeilles, Anthony Diosi, Albert Chaumette, François IRISA/INRIA Campus de Beaulieu F-35042 Rennes Cedex France

ISBN: (纸本)1424411807

Autonomous cars will likely play an important role in the future. A vision system designed to support outdoor navigation for such vehicles has to deal with large dynamic environments, changing imaging conditions, and temporary occlusions by other moving objects. This paper presents a novel appearance-based navigation framework relying on a single perspective vision sensor, which is aimed towards resolving of the above issues. The solution is based on a hierarchical environment representation created during a teaching stage, when the robot is controlled by a human operator. At the top level, the representation contains a graph of key-images with extracted 2D features enabling a robust navigation by visual servoing. The information stored at the bottom level enables to efficiently predict the locations of the features which are currently not visible, and eventually (re-)start their tracking. The outstanding property of the proposed framework is that it enables robust and scalable navigation without requiring a globally consistent map, even in interconnected environments. This result has been confirmed by realistic off-line experiments and successful real-time navigation trials in public urban areas. © 2007 ieee.

关键词： Image reconstruction

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Adaptive distance metrie learning for clustering

Adaptive distance metrie learning for clustering

引用

2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Ye, Jieping Zhao, Zheng Liu, Huan Computer Science and Engineering Department Arizona State University

ISBN: (纸本)1424411807

A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metric learning algorithms appeal to projecting observed data onto a low-dimensional manifold, where geometric relationships such as local or global pairwise distances are preserved. However, the projection may not necessarily improve the separability of the data, which is the desirable outcome of clustering. In this paper, we propose a novel unsupervised adaptive Metric Learning algorithm, called AML, which performs clustering and distance metric learning simultaneously. AML projects the data onto a low-dimensional manifold, where the separability of the data is maximized. We show that the joint clustering and distance metric learning can be formulated as a trace maximization problem, which can be solved via an iterative procedure in the EM framework. Experimental results on a collection of benchmark data sets demonstrated the effectiveness of the proposed algorithm. © 2007 ieee.

关键词： Learning systems

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Tracking as repeated figure/ground segmentation

Tracking as repeated figure/ground segmentation

引用

2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Ren, Xiaofeng Malik, Jitendra Toyota Technological Institute at Chicago 1427 E. 60th Street Chicago IL 60637 Computer Science Division University of California Berkeley CA 94720

ISBN: (纸本)1424411807

Tracking over a long period of time is challenging as the appearance, shape and scale of the object in question may vary. We propose a paradigm of tracking by repeatedly segmenting figure from background. Accurate spatial support obtained in segmentation provides rich information about the track and enables reliable tracking of non-rigid objects without drifting. Figure/ground segmentation operates sequentially in each frame by utilizing both static image cues and temporal coherence cues, which include an appearance model of brightness (or color) and a spatial model propagating figure/ground masks through low-level region correspondence. A superpixel-based conditional random field linearly combines cues and loopy belief propagation is used to estimate marginal posteriors of figure vs background. We demonstrate our approach on long sequences of sports video, including figure skating and football. © 2007 ieee.

关键词： Image segmentation

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Efficient image processing with the apply language

Efficient image processing with the apply language

引用

Australian pattern recognition society (APRS)

作者： Hamey, Leonard G.C. Macquarie University Sydney Australia

ISBN: (纸本)0769530672

Apply is a Domain-Specific Language for image processing and low-level computer vision. Apply allows programmers to write kernel operations that focus on the computation for a single pixel location. The compiler generates code to perform the kernel computation over entire images. The original Apply implementation was developed 20 years ago for efficient processing on parallel architectures. The current-generation Apply compiler targets efficient code generation for general-purpose computers, typically outperforming handwritten code, while maintaining the simplicity of the original language. The use of modem compiler writing tools, specifically Stratego/XT, has facilitated improvements in the language design and made it easy to target the compiler to different environments. A large number of computer vision and image processing operations can be expressed in Apply. However, some algorithms require additional features. To motivate future language development, we analyse the requirements of the algorithms provided in a commercial machine vision library. © 2007 ieee.

关键词： computer vision

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Multiple-view face tracking for modeling and analysis based on non-cooperative video imagery

Multiple-view face tracking for modeling and analysis based ...

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2007 ieee computer society conference on computer vision and pattern recognition, CVPR'07

作者： Von Duhn, Scott Yin, Lijun Ko, Myung Jin Hung, Terry Department of Computer Science State University of New York at Binghamton Binghamton NY 13902

ISBN: (纸本)1424411807

3D face analysis has been researched intensively in recent decades. Most 3D data (so called range facial data) are obtained from 3D range imaging systems. Such data representations have been proven effective for face recognition in 3D space. However, obtaining such data requires subject cooperation in a constrained environment, which is not practical for many real applications of video surveillance. It is therefore in high demand to use regular video cameras to generate 3D face models for further classification. The goal of our research is to develop a method of tracking feature points on a face in multiple views in order to build 3D models of individual faces. We proposed a three-view based video tracking and model creation algorithm, which is based on the Active Appearance Model and a generic facial model. We will describe how to build useful individual models over time, and validate the created dynamic model sequences through the application of face recognition. Tracking multiple view fiducial points of a face in a time sequence can also be used for facial expression analysis. Our experiments demonstrated the feasibility of the proposed work. © 2007 ieee.

关键词： Face recognition

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