2009 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第4页

SIFT-Rank: Ordinal description for invariant feature correspondence SIFT-Rank:不变特征对应的序数描述

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206849

M. Toews, W. Wells

引用次数: 61

Nonparametric discriminant HMM and application to facial expression recognition 非参数判别HMM及其在面部表情识别中的应用

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206509

Lifeng Shang, Kwok-Ping Chan

引用次数: 67

Geometric and probabilistic image dissimilarity measures for common field of view detection 通用视场检测的几何和概率图像不相似度量

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206810

Marcel Brückner, Ferid Bajramovic, Joachim Denzler

{"title":"Geometric and probabilistic image dissimilarity measures for common field of view detection","authors":"Marcel Brückner, Ferid Bajramovic, Joachim Denzler","doi":"10.1109/CVPR.2009.5206810","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206810","url":null,"abstract":"Detecting image pairs with a common field of view is an important prerequisite for many computer vision tasks. Typically, common local features are used as a criterion for identifying such image pairs. This approach, however, requires a reliable method for matching features, which is generally a very difficult problem, especially in situations with a wide baseline or ambiguities in the scene. We propose two new approaches for the common field of view problem. The first one is still based on feature matching. Instead of requiring a very low false positive rate for the feature matching, however, geometric constraints are used to assess matches which may contain many false positives. The second approach completely avoids hard matching of features by evaluating the entropy of correspondence probabilities. We perform quantitative experiments on three different hand labeled scenes with varying difficulty. In moderately difficult situations with a medium baseline and few ambiguities in the scene, our proposed methods give similarly good results to the classical matching based method. On the most challenging scene having a wide baseline and many ambiguities, the performance of the classical method deteriorates, while ours are much less affected and still produce good results. Hence, our methods show the best overall performance in a combined evaluation.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133324071","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: 9

Learning signs from subtitles: A weakly supervised approach to sign language recognition 从字幕中学习手语:一个弱监督的手语识别方法

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206647

H. Cooper, R. Bowden

引用次数: 76

Relighting objects from image collections 重新照亮图像集合中的对象

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206753

Tom Haber, Christian Fuchs, P. Bekaert, H. Seidel, M. Goesele, H. Lensch

引用次数: 108

Robust multi-class transductive learning with graphs 基于图的鲁棒多类转换学习

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206871

W. Liu, Shih-Fu Chang

{"title":"Robust multi-class transductive learning with graphs","authors":"W. Liu, Shih-Fu Chang","doi":"10.1109/CVPR.2009.5206871","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206871","url":null,"abstract":"Graph-based methods form a main category of semi-supervised learning, offering flexibility and easy implementation in many applications. However, the performance of these methods is often sensitive to the construction of a neighborhood graph, which is non-trivial for many real-world problems. In this paper, we propose a novel framework that builds on learning the graph given labeled and unlabeled data. The paper has two major contributions. Firstly, we use a nonparametric algorithm to learn the entire adjacency matrix of a symmetry-favored k-NN graph, assuming that the matrix is doubly stochastic. The nonparametric algorithm makes the constructed graph highly robust to noisy samples and capable of approximating underlying submanifolds or clusters. Secondly, to address multi-class semi-supervised classification, we formulate a constrained label propagation problem on the learned graph by incorporating class priors, leading to a simple closed-form solution. Experimental results on both synthetic and real-world datasets show that our approach is significantly better than the state-of-the-art graph-based semi-supervised learning algorithms in terms of accuracy and robustness.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751541","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: 166

Pictorial structures revisited: People detection and articulated pose estimation 重新审视图像结构:人物检测和关节姿态估计

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206754

Mykhaylo Andriluka, S. Roth, B. Schiele

{"title":"Pictorial structures revisited: People detection and articulated pose estimation","authors":"Mykhaylo Andriluka, S. Roth, B. Schiele","doi":"10.1109/CVPR.2009.5206754","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206754","url":null,"abstract":"Non-rigid object detection and articulated pose estimation are two related and challenging problems in computer vision. Numerous models have been proposed over the years and often address different special cases, such as pedestrian detection or upper body pose estimation in TV footage. This paper shows that such specialization may not be necessary, and proposes a generic approach based on the pictorial structures framework. We show that the right selection of components for both appearance and spatial modeling is crucial for general applicability and overall performance of the model. The appearance of body parts is modeled using densely sampled shape context descriptors and discriminatively trained AdaBoost classifiers. Furthermore, we interpret the normalized margin of each classifier as likelihood in a generative model. Non-Gaussian relationships between parts are represented as Gaussians in the coordinate system of the joint between parts. The marginal posterior of each part is inferred using belief propagation. We demonstrate that such a model is equally suitable for both detection and pose estimation tasks, outperforming the state of the art on three recently proposed datasets.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131462900","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: 877

Interval HSV: Extracting ink annotations 间隔HSV:提取墨水注释

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206554

John C. Femiani, A. Razdan

引用次数: 9

Beyond the graphs: Semi-parametric semi-supervised discriminant analysis 图外:半参数半监督判别分析

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206675

Fei Wang, Xin Wang, Ta-Hsin Li

{"title":"Beyond the graphs: Semi-parametric semi-supervised discriminant analysis","authors":"Fei Wang, Xin Wang, Ta-Hsin Li","doi":"10.1109/CVPR.2009.5206675","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206675","url":null,"abstract":"Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data, which makes the estimated projection direction inaccurate. To address the above limitations, in this paper, we propose a novel semi-supervised discriminant analysis approach. Unlike traditional graph based methods, our algorithm incorporates the geometric information revealed by both labeled and unlabeled data points in a semi-parametric way. Specifically, the final projections of the data points will contain two parts: a discriminant part learned by traditional LDA (or KDA) on the labeled points and a geometrical part learned by kernel PCA on the whole data set. Therefore we call our algorithm semi-parametric semi-supervised discriminant analysis (SSDA). Experimental results on face recognition and image retrieval tasks are presented to show the effectiveness of our method.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127650339","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: 6

Fourier analysis and Gabor filtering for texture analysis and local reconstruction of general shapes 傅里叶分析和Gabor滤波用于纹理分析和一般形状的局部重建

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206591

Fabio Galasso, Joan Lasenby

引用次数: 6