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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ee Hui Lim David Suter Institute for Vision System Engineering Monash University Clayton VIC Australia

In this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suterpsilas methods by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved recognition rate. In the model, the raw data points are over-segmented into an improved mid-level representation, ldquosuper-voxelsrdquo. Local and regional features are then extracted from the super-voxel and parameters learnt by the multi-scale Conditional Random Fields. The classification accuracy is improved by 5% to 10% with our proposed model compared to labeling with Conditional Random Fields in (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lower than the previous 3D data labeling approaches.

关键词： Clouds Graphical models Labeling Feature extraction Data mining Laser modes Laser radar Machine vision Data engineering Systems engineering and theory

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Graph Commute Times for Image Representation

Graph Commute Times for Image Representation

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26th IEEE Conference on computer vision and pattern recognition (CVPR 2008), vol.12

作者： Regis Behmo Nikos Paragios Veronique Prinet Ecole Centrale Paris Malabry France NLPR/LIAMA Institute of Automation Chinese Academy and Sciences Beijing China

We introduce a new image representation that encompasses both the general layout of groups of quantized local invariant descriptors as well as their relative frequency. A graph of interest points clusters is constructed and we use the matrix of commute times between the different nodes of the graph to obtain a description of their relative arrangement that is robust to large intra class variation. The obtained high dimensional representation is then embedded in a space of lower dimension by exploiting the spectral properties of the graph made of the different images. Classification tasks can be performed in this embedding space. We expose classification and labelling results obtained on three different datasets, including the challenging PASCAL VOC2007 dataset. The performances of our approach compare favorably with the standard bag of features, which is a particular case of our representation.

关键词： Image representation Labeling Frequency Robustness computer vision Laplace equations Automation Shape Application software Graph theory

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A Discriminatively Trained, Multiscale, Deformable Part Model

A Discriminatively Trained, Multiscale, Deformable Part Mode...

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26th IEEE Conference on computer vision and pattern recognition (CVPR 2008), vol.7

作者： Pedro Felzenszwalb David McAllester Deva Ramanan University of Chicago USA Toyota Technological Institute Chicago USA University of California Irvine USA

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

关键词： Deformable models Filters Support vector machines Object detection Data mining Spatial resolution Histograms computer vision Support vector machine classification Costs

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26th IEEE Conference on computer vision and pattern recognition (CVPR 2008), vol.7

作者： Yan Liu Florent Perronnin Xerox Research Centre Europe (XRCE) Textual and Visual Pattern Analysis (TVPA)

We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common "universal" GMM using the maximum a posteriori (MAP) criterion. The advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. This work is applied to the image categorization problem: images are modeled as bags of low-level features and classification is performed using a kernel classifier based on the proposed similarity measure. Experimental results on the PASCAL VOC 2006 and VOC 2007 databases show the excellent performance of our approach.

关键词： Vocabulary Costs Maximum likelihood estimation Kernel pattern analysis Distributed computing Performance evaluation Histograms Feature extraction Sampling methods

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The IV2 multimodal biometric database (including Iris, 2D, 3D, stereoscopic, and talking face data), and the IV2-2007 evaluation campaign

The IV2 multimodal biometric database (including Iris, 2D, 3...

引用

BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems

作者： Petrovska-Delacrétaz, D. Lelandais, S. Colineau, J. Chen, L. Dorizzi, B. Ardabilian, M. Krichen, E. Mellakh, M.-A. Chaari, A. Guerfi, S. D'Hose, J. Amor, B. Ben Institut TELECOM TandM SudParis 9 Rue Charles Fourrier Evry France Laboratoire IBISC-CNRS PRE 2873 Université d'Evry Val d'Essonne Evry France THALES Research and Technology RD 128 91767 Palaiseau Cedex France Ecole Centrale de Lyon Lyon France Telecom-Lillel Lille France

ISBN: (纸本)9781424427307

Face recognition finds its place in a large number of applications. They occur in different contexts related to security, entertainment or Internet applications. Reliable face recognition is still a great challenge to computer vision and pattern recognition researchers, and new algorithms need to be evaluated on relevant databases. The publicly available TV2 database allows monomodal and multimodal experiments using face data. Known variabilities, that are critical for the performance of the biometric systems (such as pose, expression, illumination and quality) are present. The face and subface data that are acquired in this database are: 2D audio-video talking-face sequences, 2D stereoscopic data acquired with two pairs of synchronized cameras, 3D facial data acquired with a laser scanner, and iris images acquired with a portable infrared camera. The TV2 database is designed for monomodal and multimodal experiments. The quality of the acquired data is of great importance. Therefore as a first step, and in order to better evaluate the quality of the data, a first internal evaluation was conducted. Only a small amount of the total acquired data was used for this evaluation: 2D still images, 3D scans and iris images. First results show the interest of this database. In parallel to the research algorithms, open-source reference systems were also run for baseline comparisons. © 2008 IEEE.

关键词： Database systems

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The IV2 Multimodal Biometric Database (Including Iris, 2D, 3D, Stereoscopic, and Talking Face Data), and the IV2-2007 Evaluation Campaign

The IV2 Multimodal Biometric Database (Including Iris, 2D, 3...

引用

IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： D. Petrovska-Delacretaz S. Lelandais J. Colineau L. Chen B. Dorizzi M. Ardabilian E. Krichen M.-A. Mellakh A. Chaari S. Guerfi J. D'Hose B. Ben Amor T&M SudParis Institut TELECOM FRANCE Laboratoire IBISC-CNRS FRE 2873 Universit' d'Evry Val d'Essonne Evry FRANCE THALES Research and Technology Palaiseau Cedex FRANCE Ecole Centrale de Lyon Lyon FRANCE Telecom-Lillel Lille FRANCE

Face recognition finds its place in a large number of applications. They occur in different contexts related to security, entertainment or Internet applications. Reliable face recognition is still a great challenge to computer vision and pattern recognition researchers, and new algorithms need to be evaluated on relevant databases. The publicly available IV 2 database allows monomodal and multimodal experiments using face data. Known variabilities, that are critical for the performance of the biometric systems (such as pose, expression, illumination and quality) are present. The face and subface data that are acquired in this database are: 2D audio-video talking-face sequences, 2D stereoscopic data acquired with two pairs of synchronized cameras, 3D facial data acquired with a laser scanner, and iris images acquired with a portable infrared camera. The IV 2 database is designed for monomodal and multimodal experiments. The quality of the acquired data is of great importance. Therefore as a first step, and in order to better evaluate the quality of the data, a first internal evaluation was conducted. Only a small amount of the total acquired data was used for this evaluation: 2D still images, 3D scans and iris images. First results show the interest of this database. In parallel to the research algorithms, open-source reference systems were also run for baseline comparisons.

关键词： Image databases Face recognition Application software Cameras Iris Data security Internet computer vision pattern recognition Face detection

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2007 IEEE computer Society Conference on computer vision and pattern recognition, CVPR'07

2007 IEEE Computer Society Conference on Computer Vision and...

引用

2007 IEEE computer Society Conference on computer vision and pattern recognition, CVPR'07

ISBN: (纸本)1424411807

The proceedings contain 525 papers. The topics discussed include: learning visual similarity measures for comparing never seen objects;a contextual dissimilarity measure for accurate and efficient image search;learning local image descriptors;principal curvature-based region detector for object recognition;a benchmark for the comparison of 3-D motion segmentation algorithms;a nonparametric treatment for location/segmentation based visual tracking;learning gaussian conditional random fields for low-level vision;hierarchical structuring of data on manifolds;learning GMRF structures for spatial priors;element rearrangement for tensor-based subspace learning;unsupervised clustering using multi-resolution perceptual grouping;object tracking by asymmetric kernel mean shift with automatic scale and orientation selection;and free-form nonrigid image registration using generalized elastic nets.

关键词： computer vision

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Proceedings of the 2007 International Conference on Image Processing, computer vision, and pattern recognition, IPCV 2007

Proceedings of the 2007 International Conference on Image Pr...

引用

2007 International Conference on Image Processing, computer vision, and pattern recognition, IPCV 2007

ISBN: (纸本)9781601320438

The proceedings contain 95 papers. The topics discussed include: a novel level-independent fingerprint segmentation algorithm based on Fourier energy;towards an automated end-to-end face recognition system;a computationally efficient fingerprint segmentation algorithm using digital closed curves;grouping, segmentation and recognition of handwritten social security images;a multi-level component-based approach for face recognition;handwritten and typewritten word and character separation in unconstrained document images;recognition of handwritten Arabic writing with Kohonen networks;tracking multiple sports players for mobile display;genetic eye tracking for blinking;and target tracking and posture estimation of 3D objects by using particle filter and parametric eigenspace method.

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Energy Minimization Methods in computer vision and pattern recognition: 6th International Conference, EMMCVPR 2007 Proceedings

引用

6th International Conference on Energy Minimization Methods in computer vision and pattern recognition, EMMCVPR 2007

ISBN: (纸本)9783540741954

The proceedings contain 37 papers. The topics discussed include: an effective multi-level algorithm based on simulated annealing for bisecting graph;exact solution of permuted submodular MinSum problems;efficient shape matching via graph cuts;simulating classics mosaics with graph cuts;an energy minimisation approach to attributed graph regularisation;a pupil localization algorithm based on adaptive gabor filtering and navigating radial symmetry;decomposing document images by heuristic search;skew detection based on elongate feature;active appearance models fitting with occlusion;combining left and right irises for personal authentication;bottom-up recognition and parsing of the human body;an automatic portrait system based on and-or graph representation;and object category recognition using generative template boosting.

关键词： Energy management

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2007 IEEE computer Society Conference on computer vision and pattern recognition: Preface

Proceedings of the IEEE Computer Society Conference on Compu...

引用

Proceedings of the IEEE computer Society Conference on computer vision and pattern recognition 2007年

作者： Zhu, Zhigang Huang, Thomas S. Tian, Ying-Li City College City University of New York University of Illinois Urbana-Champaign IBM T.J. Watson Research Center

No abstract available

ISBN: (纸本)1424411807

No abstract available

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