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Fast Human Detection in Crowded Scenes by Contour Integration and Local Shape Estimation

Fast Human Detection in Crowded Scenes by Contour Integratio...

引用

ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Beleznai, Csaba Bischof, Horst Austrian Res Ctr GmbH ARC Vienna Austria Graz Univ Technol Inst Comp Graph & Vision A-8010 Graz Austria

ISBN: (纸本)9781424439928

The complexity of human detection increases significantly with a growing density of humans populating a scene. This paper presents a Bayesian detection framework using shape and motion cues to obtain a maximum a posteriori (MAP) solution for human configurations consisting of many, possibly occluded pedestrians viewed by a stationary camera. The paper contains two novel contributions for the human detection task: 1. computationally efficient detection based on shape templates using contour integration by means of integral images which are built by oriented string scans;(2) a non-parametric approach using an approximated version of the Shape Context descriptor which generates informative object parts and infers the presence of humans despite occlusions. The outputs of the two detectors are used to generate a spatial configuration of hypothesized human body locations. The configuration is iteratively optimized while taking into account the depth ordering and occlusion status of the hypotheses. The method achieves fast computation times even in complex scenarios with a high density of people. Its validity is demonstrated on a substantial amount of image data using the CAVIAR and our own datasets. Evaluation results and comparison with state of the art are presented.

关键词： Iterative methods

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A Family of Contextual Measures of Similarity between Distributions with Application to Image Retrieval

A Family of Contextual Measures of Similarity between Distri...

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ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Perronnin, Florent Liu, Yan Renders, Jean-Michel Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe (XRCE) France

ISBN: (纸本)9781424439928

We introduce a novel family of contextual measures of similarity between distributions: the similarity between two distributions q and p is measured in the context of a third distribution u. In our framework any traditional measure of similarity / dissimilarity has its contextual counterpart. We show that for two important families of divergences (Bregman and Csiszar), the contextual similarity computation consists in solving a convex optimization problem. We focus on the case of multinomials and explain how to compute in practice the similarity for several well-known measures. These contextual measures are then applied to the image retrieval problem. In such a case, the context u is estimated from the neighbors of a query q. One of the main benefits of our approach lies in the fact that using different contexts, and especially contexts at multiple scales (i.e. broad and narrow contexts), provides different views on the same problem. Combining the different views can improve retrieval accuracy. We will show on two very different datasets (one of photographs, the other of document images) that the proposed measures have a relatively small positive impact on macro Average Precision (which measures purely ranking) and a large positive impact on micro Average Precision (which measures both ranking and consistency of the scores across multiple queries).

关键词： Image retrieval

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Appearance learning by adaptive kaiman filters for FLIR tracking

Appearance learning by adaptive kaiman filters for FLIR trac...

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2009 ieee computer society conference on computer vision and pattern recognition workshops, CVPR workshops 2009

作者： Venkataraman, Vijay Fan, Guoliang Fan, Xin Havlicek, Joseph P. School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States School of Electrical and Computer Engineering University of Oklahoma Norman OK 73019 United States

ISBN: (纸本)9781424439911

This paper addresses the challenging issue of target tracking and appearance learning in Forward Looking Infrared (FLIR) sequences. Tracking and appearance learning are formulated as a joint state estimation problem with two parallel inference processes. Specifically, a new adaptive Kaiman filter is proposed to learn histogram-based target appearances. A particle filter is used to estimate the target position and size, where the learned appearance plays an important role. Our appearance learning algorithm is compared against two existing methods and experiments on the AMCOM FLIR dataset validate its effectiveness. © 2009 ieee.

关键词： Target tracking

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A Nonparametric Riemannian Framework for Processing High Angular Resolution Diffusion Images (HARDI)

A Nonparametric Riemannian Framework for Processing High Ang...

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ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Goh, Alvina Lenglet, Christophe Thompson, Paul M. Vidal, Rene Johns Hopkins Univ Baltimore MD 21218 USA Univ Minnesota Minneapolis MN USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781424439928

High angular resolution diffusion imaging has become an important magnetic resonance technique for in vivo imaging. Most current research in this field focuses on developing methods for computing the orientation distribution function (ODF), which is the probability distribution function of water molecule diffusion along any angle on the sphere. In this paper, we present a Riemannian framework to carry out computations on an ODF field. The proposed framework does not require that the ODFs be represented by any fixed parameterization, such as a mixture of von Mises-Fisher distributions or a spherical harmonic expansion. Instead, we use a non-parametric representation of the ODF, and exploit the fact that under the square-root re-parameterization, the space of ODFs forms a Riemannian manifold, namely the unit Hilbert sphere. Specifically, we use Riemannian operations to perform various geometric data processing algorithms, such as interpolation, convolution and linear and nonlinear filtering. We illustrate these concepts with numerical experiments on synthetic and real datasets.

关键词： computer vision

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3D Pose Estimation and Segmentation using Specular Cues

3D Pose Estimation and Segmentation using Specular Cues

引用

ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Chang, Ju Yong Raskar, Ramesh Agrawal, Amit Samsung Elect Digital Media R&D Ctr Seoul South Korea MIT Cambridge MA 02139 USA

ISBN: (纸本)9781424439928

We present a system for fast model-based segmentation and 3D pose estimation of specular objects using appearance based specular features. We use observed (a) specular reflection and (b) specular flow as cues, which are matched against similar cues generated from a CAD model of the object in various poses. We avoid estimating 3D geometry or depths, which is difficult and unreliable for specular scenes. In the first method, the environment map of the scene is utilized to generate a database containing synthesized specular reflections of the object for densely sampled 3D poses. This database is compared with captured images of the scene at run time to locate and estimate the 3D pose of the object. In the second method, specular flows are generated for dense 3D poses as illumination invariant features and are matched to the specular flow of the scene. We incorporate several practical heuristics such as use of saturated/highlight pixels for fast matching and normal selection to minimize the effects of inter-reflections and cluttered backgrounds. Despite its simplicity, our approach is effective in scenes with multiple specular objects, partial occlusions, inter-reflections, cluttered backgrounds and changes in ambient illumination. Experimental results demonstrate the effectiveness of our method for various synthetic and real objects.

关键词： computer aided design

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Dominance detection in face-to-face conversations

Dominance detection in face-to-face conversations

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2009 ieee computer society conference on computer vision and pattern recognition workshops, CVPR workshops 2009

作者： Escalera, Sergio Martínez, Rosa M. Vitrià, Jordi Radeva, Petia Anguera, M. Teresa Dept. Matemàtiques Universitat de Barcelona Gran Via de les Corts 585 08007 Barcelona Spain Dept. Metodologia de les Ciències Del Comportament Universitat de Barcelona Gran Via de les Corts 585 08007 Barcelona Spain Computer Vision Center Campus UAB Edifici O 08193 Bellaterra Barcelona Spain

ISBN: (纸本)9781424439911

Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences. © 2009 ieee.

关键词： Face recognition

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Towards Geographical Referencing of Monocular SLAM Reconstruction Using 3D City Models: Application to Real-Time Accurate vision-Based Localization

Towards Geographical Referencing of Monocular SLAM Reconstru...

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ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Lothe, Pierre Bourgeois, Steve Dekeyser, Fabien Royer, Eric Dhome, Michel CEA LIST Boite Courrier 94 F-91191 Gif Sur Yvette France UBP CNRS LASMEA F-63177 Clermont Ferrand France

ISBN: (纸本)9781424439928

In the past few years, lots of works were achieved on Simultaneous Localization and Mapping (SLAM). It is now possible to follow in real time the trajectory of a moving camera in an unknown environment. However, current SLAM methods are still prone to drift errors, which prevent their use in large-scale applications. In this paper, we propose a solution to reduce those errors a posteriori. Our solution is based on a post-processing algorithm that exploits additional geometric constraints, relative to the environment, to correct both the reconstructed geometry and the camera trajectory. These geometric constraints are obtained through a coarse 3D modelisation of the environment, similar to those provided by GIS database. First, we propose an original articulated transformation model in order to roughly align the SLAM reconstruction with this 3D model through a non-rigid ICP step. Then, to refine the reconstruction, we introduce a new bundle adjustment cost function that includes, in a single term, the usual 3D point/2D observation consistency constraint as well as the geometric constraints provided by the 3D model. Results on large-scale synthetic and real sequences show that our method successfully improves SLAM reconstructions. Besides, experiments prove that the resulting reconstruction is accurate enough to be directly used for global relocalization applications.

关键词： Cost functions

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Learning Semantic Scene Models by Object Classification and Trajectory Clustering

Learning Semantic Scene Models by Object Classification and ...

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ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Zhang, Tianzhu Lu, Hanqing Li, Stan Z. Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun Donglu Beijing Peoples R China Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing Peoples R China

ISBN: (纸本)9781424439928

Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects' category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian Mixture Model (GMM), and supplied to this graph. The graph-cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real-world scenes show the validity of our proposed method.

关键词： Semantics

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Shape Google: A computer vision approach to isometry invariant shape retrieval

Shape Google: A computer vision approach to isometry invaria...

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2009 ieee 12th International conference on computer vision workshops, ICCV workshops 2009

作者： Ovsjanikov, Maks Bronstein, Alexander M. Bronstein, Michael M. Guibas, Leonidas J. ICME Stanford University United States Dept. of Computer Science Technion Israel Dept. of Computer Science Stanford University United States

Feature-based methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approac... 详细信息

ISBN: (纸本)9781424444427

关键词： computer vision

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Echocardiography contour extraction with local and global priors through boosting and level sets

Echocardiography contour extraction with local and global pr...

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2009 ieee computer society conference on computer vision and pattern recognition workshops, CVPR workshops 2009

作者： Oktay, Ayse Betul Akgul, Yusuf Sinan GIT Vision Lab Turkey Department of Computer Engineering Gebze Institute of Technology Gebze Kocaeli 41400 Turkey

ISBN: (纸本)9781424439911

Extracting endocardium and epicardium from echocardiographic images is a challenging task because of large amounts of noise, signal drop-out, unrelated structures, and unseen wall parts. This paper introduces a new technique that automatically extracts cardiac borders by incorporating local and global priors through boosting and level set methods. The shape-based global prior is incorporated into the system by regularly re-initializing the level set surface under the influence of the expert detected contours. The local priors with image and temporal information are learned through boosting. The proposed system has many advantages. First, boosting encodes the knowledge about the image information and the temporal cardiac wall motion effectively by using spatiotemporal filters. Second, the local priors can use any features from the images including different filters and intensity profiles. Furthermore, other hard constraints like local shape, texture, distance, etc. can be added to local features effectively. The system is validated on echocardiograms and the results are found to be promising. © 2009 ieee.

关键词： Echocardiography

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