2007 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第2页

Secure Biometric Templates from Fingerprint-Face Features 从指纹-面部特征安全的生物识别模板

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383385

Y. Sutcu, Qiming Li, N. Memon

引用次数: 129

Learning Color Names from Real-World Images 从真实世界的图像中学习颜色名称

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383218

Joost van de Weijer, C. Schmid, J. Verbeek

引用次数: 199

High-Speed Measurement of BRDF using an Ellipsoidal Mirror and a Projector 利用椭球镜和投影仪高速测量BRDF

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383467

Y. Mukaigawa, K. Sumino, Y. Yagi

引用次数: 38

A Nine-point Algorithm for Estimating Para-Catadioptric Fundamental Matrices 拟反射性基本矩阵的九点估计算法

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383065

Christopher Geyer, Henrik Stewénius

引用次数: 28

Partially Occluded Object-Specific Segmentation in View-Based Recognition 基于视图的识别中部分遮挡的特定对象分割

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383268

Minsu Cho, Kyoung Mu Lee

{"title":"Partially Occluded Object-Specific Segmentation in View-Based Recognition","authors":"Minsu Cho, Kyoung Mu Lee","doi":"10.1109/CVPR.2007.383268","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383268","url":null,"abstract":"We present a novel object-specific segmentation method which can be used in view-based object recognition systems. Previous object segmentation approaches generate inexact results especially in partially occluded and cluttered environment because their top-down strategies fail to explain the details of various specific objects. On the contrary, our segmentation method efficiently exploits the information of the matched model views in view-based recognition because the aligned model view to the input image can serve as the best top-down cue for object segmentation. In this paper, we cast the problem of partially occluded object segmentation as that of labelling displacement and foreground status simultaneously for each pixel between the aligned model view and an input image. The problem is formulated by a maximum a posteriori Markov random field (MAP-MRF) model which minimizes a particular energy function. Our method overcomes complex occlusion and clutter and provides accurate segmentation boundaries by combining a bottom-up segmentation cue together. We demonstrate the efficiency and robustness of it by experimental results on various objects under occluded and cluttered environments.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Robust Warping Method for Fingerprint Matching 一种用于指纹匹配的鲁棒翘曲方法

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383391

Dongjin Kwon, I. Yun, Sang Uk Lee

引用次数: 6

Scaled Motion Dynamics for Markerless Motion Capture 缩放运动动力学无标记运动捕捉

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383128

B. Rosenhahn, T. Brox, H. Seidel

引用次数: 63

Bilattice-based Logical Reasoning for Human Detection 基于双栅格的人类检测逻辑推理

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383133

V. Shet, J. Neumann, Visvanathan Ramesh, L. Davis

{"title":"Bilattice-based Logical Reasoning for Human Detection","authors":"V. Shet, J. Neumann, Visvanathan Ramesh, L. Davis","doi":"10.1109/CVPR.2007.383133","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383133","url":null,"abstract":"The capacity to robustly detect humans in video is a critical component of automated visual surveillance systems. This paper describes a bilattice based logical reasoning approach that exploits contextual information and knowledge about interactions between humans, and augments it with the output of different low level detectors for human detection. Detections from low level parts-based detectors are treated as logical facts and used to reason explicitly about the presence or absence of humans in the scene. Positive and negative information from different sources, as well as uncertainties from detections and logical rules, are integrated within the bilattice framework. This approach also generates proofs or justifications for each hypothesis it proposes. These justifications (or lack thereof) are further employed by the system to explain and validate, or reject potential hypotheses. This allows the system to explicitly reason about complex interactions between humans and handle occlusions. These proofs are also available to the end user as an explanation of why the system thinks a particular hypothesis is actually a human. We employ a boosted cascade of gradient histograms based detector to detect individual body parts. We have applied this framework to analyze the presence of humans in static images from different datasets.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116393667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 124

Learning the Compositional Nature of Visual Objects 学习视觉对象的组成性质

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383154

B. Ommer, J. Buhmann

引用次数: 60

The Hierarchical Isometric Self-Organizing Map for Manifold Representation 流形表示的层次等距自组织映射

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383402

Haiying Guan, M. Turk

{"title":"The Hierarchical Isometric Self-Organizing Map for Manifold Representation","authors":"Haiying Guan, M. Turk","doi":"10.1109/CVPR.2007.383402","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383402","url":null,"abstract":"We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimensional input data in a low dimensional space. The main contribution of our algorithm is threefold. First, we modify the previous ISOSOM algorithm by a local linear interpolation (LLl) technique, which maps the data samples from low dimensional space back to high dimensional space and makes the complete mapping pseudo-invertible. The modified-ISOSOM (M-ISOSOM) follows the global geometric structure of the data, and also preserves local geometric relations to reduce the nonlinear mapping distortion and make the learning more accurate. Second, we propose the H-ISOSOM algorithm for the computational complexity problem of Isomap, SOM and LLI and the nonlinear complexity problem of the highly twisted manifold. H-ISOSOM learns an organized structure of a non-convex, large scale manifold and represents it by a set of hierarchical organized maps. The hierarchical structure follows a coarse-to-fine strategy. According to the coarse global structure, it \"unfolds \" the manifold at the coarse level and decomposes the sample data into small patches, then iteratively learns the nonlinearity of each patch in finer levels. The algorithm simultaneously reorganizes and clusters the data samples in a low dimensional space to obtain the concise representation. Third, we give quantitative comparisons of the proposed method with similar methods on standard data sets. Finally, we apply H-ISOSOM to the problem of appearance-based hand pose estimation. Encouraging experimental results validate the effectiveness and efficiency of H-ISOSOM.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121881890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8