2007 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第10页

Patch-based Image Correlation with Rapid Filtering 基于patch的快速滤波图像相关

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383373

G. Guo, C. Dyer

引用次数: 38

Sensor noise modeling using the Skellam distribution: Application to the color edge detection 使用Skellam分布的传感器噪声建模:在颜色边缘检测中的应用

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383004

Youngbae Hwang, Jun-Sik Kim, In-So Kweon

引用次数: 53

Pose and Illumination Invariant Face Recognition in Video 视频中姿态和光照不变人脸识别

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383376

Yilei Xu, A. Roy-Chowdhury, Keyur Patel

引用次数: 20

Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data 判别聚类改进:在有限训练数据下改进目标类别识别

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383270

Liu Yang, Rong Jin, C. Pantofaru, R. Sukthankar

{"title":"Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data","authors":"Liu Yang, Rong Jin, C. Pantofaru, R. Sukthankar","doi":"10.1109/CVPR.2007.383270","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383270","url":null,"abstract":"A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a significant shortcoming of this approach is that the clustering and classification are disconnected. Since the clustering into visual words is unsupervised, the representation does not necessarily capture the aspects of the data that are most useful for classification. More seriously, the semantic relationship between clusters is lost, causing the overall classification performance to suffer. We introduce \"discriminative cluster refinement\" (DCR), a method that explicitly models the pairwise relationships between different visual words by exploiting their co-occurrence information. The assigned class labels are used to identify the co-occurrence patterns that are most informative for object classification. DCR employs a maximum-margin approach to generate an optimal kernel matrix for classification. One important benefit of DCR is that it integrates smoothly into existing bag-of-words information retrieval systems by employing the set of visual words generated by any clustering method. While DCR could improve a broad class of information retrieval systems, this paper focuses on object category recognition. We present a direct comparison with a state-of-the art method on the PASCAL 2006 database and show that cluster refinement results in a significant improvement in classification accuracy given a small number of training examples.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"15 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":"123008055","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: 18

On the Performance Prediction and Validation for Multisensor Fusion 多传感器融合性能预测与验证研究

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383112

Rong Wang, B. Bhanu

引用次数: 1

Adaptive Distance Metric Learning for Clustering 聚类的自适应距离度量学习

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383103

Jieping Ye, Zheng Zhao, Huan Liu

引用次数: 119

Human Detection via Classification on Riemannian Manifolds 黎曼流形的分类人体检测

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383197

Oncel Tuzel, F. Porikli, P. Meer

引用次数: 543

On Constant Focal Length Self-Calibration From Multiple Views 多视角定焦距自标定研究

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383066

B. Bocquillon, A. Bartoli, Pierre Gurdjos, Alain Crouzil

{"title":"On Constant Focal Length Self-Calibration From Multiple Views","authors":"B. Bocquillon, A. Bartoli, Pierre Gurdjos, Alain Crouzil","doi":"10.1109/CVPR.2007.383066","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383066","url":null,"abstract":"We investigate the problem of finding the metric structure of a general 3D scene viewed by a moving camera with square pixels and constant unknown focal length. While the problem has a concise and well-understood formulation in the stratified framework thanks to the absolute dual quadric, two open issues remain. The first issue concerns the generic Critical Motion Sequences, i.e. camera motions for which self-calibration is ambiguous. Most of the previous work focuses on the varying focal length case. We provide a thorough study of the constant focal length case. The second issue is to solve the nonlinear set of equations in four unknowns arising from the dual quadric formulation. Most of the previous work either does local nonlinear optimization, thereby requiring an initial solution, or linearizes the problem, which introduces artificial degeneracies, most of which likely to arise in practice. We use interval analysis to solve this problem. The resulting algorithm is guaranteed to find the solution and is not subject to artificial degeneracies. Directly using interval analysis usually results in computationally expensive algorithms. We propose a carefully chosen set of inclusion functions, making it possible to find the solution within few seconds. Comparisons of the proposed algorithm with existing ones are reported for simulated and real data.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"19 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":"132560289","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: 13

Learning a Spatially Smooth Subspace for Face Recognition 学习空间平滑子空间用于人脸识别

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383054

Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, Thomas S. Huang

{"title":"Learning a Spatially Smooth Subspace for Face Recognition","authors":"Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, Thomas S. Huang","doi":"10.1109/CVPR.2007.383054","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383054","url":null,"abstract":"Subspace learning based face recognition methods have attracted considerable interests in recently years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE), marginal fisher analysis (MFA) and local discriminant embedding (LDE). These methods consider an n1timesn2 image as a vector in Rn 1 timesn 2 and the pixels of each image are considered as independent. While an image represented in the plane is intrinsically a matrix. The pixels spatially close to each other may be correlated. Even though we have n1xn2 pixels per image, this spatial correlation suggests the real number of freedom is far less. In this paper, we introduce a regularized subspace learning model using a Laplacian penalty to constrain the coefficients to be spatially smooth. All these existing subspace learning algorithms can fit into this model and produce a spatially smooth subspace which is better for image representation than their original version. Recognition, clustering and retrieval can be then performed in the image subspace. Experimental results on face recognition demonstrate the effectiveness of our method.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"10 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":"132744373","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: 359

Unsupervised Learning of Image Transformations 图像变换的无监督学习

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383036

R. Memisevic, Geoffrey E. Hinton

引用次数: 217