2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)最新文献_第2页

Active Graph Cuts 活动图切割

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.47

Olivier Juan, Yuri Boykov

引用次数: 132

An Efficiency Criterion for 2D Shape Model Selection. 二维形状模型选择的效率准则。

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.53

Kathryn Leonard

引用次数: 2

Contour-Based Structure from Reflection 来自反射的基于轮廓的结构

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.88

Po-Hao Huang, S. Lai

{"title":"Contour-Based Structure from Reflection","authors":"Po-Hao Huang, S. Lai","doi":"10.1109/CVPR.2006.88","DOIUrl":"https://doi.org/10.1109/CVPR.2006.88","url":null,"abstract":"In this paper, we propose a novel contour-based algorithm for 3D object reconstruction from a single uncalibrated image acquired under the setting of two plane mirrors. With the epipolar geometry recovered from the image and the properties of mirror reflection, metric reconstruction of an arbitrary rigid object is accomplished without knowing the camera parameters and the mirror poses. For this mirror setup, the epipoles can be estimated from the correspondences between the object and its reflection, which can be established automatically from the tangent lines of their contours. By using the property of mirror reflection as well as the relationship between the mirror plane normal with the epipole and camera intrinsic, we can estimate the camera intrinsic, plane normals and the orientation of virtual cameras. The positions of the virtual cameras are determined by minimizing the distance between the object contours and the projected visual cone for a reference view. After the camera parameters are determined, the 3D object model is constructed via the image-based visual hulls (IBVH) technique. The 3D model can be refined by integrating the multiple models reconstructed from different views. The main advantage of the proposed contour-based Structure from Reflection (SfR) algorithm is that it can achieve metric reconstruction from an uncalibrated image without feature point correspondences. Experimental results on synthetic and real images are presented to show its performance.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114190154","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: 22

Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours 基于去相关和统计区域的活动轮廓的超声特异性分割

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.318

G. Slabaugh, Gözde B. Ünal, T. Fang, M. Wels

引用次数: 54

Efficient Optimal Kernel Placement for Reliable Visual Tracking 有效的最优内核放置可靠的视觉跟踪

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.109

Zhimin Fan, Ming Yang, Ying Wu, G. Hua, Ting Yu

引用次数: 38

BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation BoostMotion:增强用于运动估计的判别相似函数

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.73

S. Zhou, B. Georgescu, D. Comaniciu, Jie Shao

{"title":"BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation","authors":"S. Zhou, B. Georgescu, D. Comaniciu, Jie Shao","doi":"10.1109/CVPR.2006.73","DOIUrl":"https://doi.org/10.1109/CVPR.2006.73","url":null,"abstract":"Motion estimation for applications where appearance undergoes complex changes is challenging due to lack of an appropriate similarity function. In this paper, we propose to learn a discriminative similarity function based on an annotated database that exemplifies the appearance variations. We invoke the LogitBoost algorithm to selectively combine weak learners into one strong similarity function. The weak learners based on local rectangle features are constructed as nonparametric 2D piecewise constant functions, using the feature responses from both images, to strengthen the modeling power and accommodate fast evaluation. Because the negatives possess a location parameter measuring their closeness to the positives, we present a locationsensitive cascade training procedure, which bootstraps negatives for later stages of the cascade from the regions closer to the positives. This allows viewing a large number of negatives and steering the training process to yield lower training and test errors. In experiments of estimating the motion for the endocardial wall of the left ventricle in echocardiography, we compare the learned similarity function with conventional ones and obtain improved performances. We also contrast the proposed method with a learning-based detection algorithm to demonstrate the importance of temporal information in motion estimation. Finally, we insert the learned similarity function into a simple contour tracking algorithm and find that it reduces drifting.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122192496","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: 29

An Intensity-augmented Ordinal Measure for Visual Correspondence 视觉对应的增强序数测度

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.56

Anurag Mittal, Visvanathan Ramesh

{"title":"An Intensity-augmented Ordinal Measure for Visual Correspondence","authors":"Anurag Mittal, Visvanathan Ramesh","doi":"10.1109/CVPR.2006.56","DOIUrl":"https://doi.org/10.1109/CVPR.2006.56","url":null,"abstract":"Determining the correspondence of image patches is one of the most important problems in Computer Vision. When the intensity space is variant due to several factors such as the camera gain or gamma correction, one needs methods that are robust to such transformations. While the most common assumption is that of a linear transformation, a more general assumption is that the change is monotonic. Therefore, methods have been developed previously that work on the rankings between different pixels as opposed to the intensities themselves. In this paper, we develop a new matching method that improves upon existing methods by using a combination of intensity and rank information. The method considers the difference in the intensities of the changed pixels in order to achieve greater robustness to Gaussian noise. Furthermore, only uncorrelated order changes are considered, which makes the method robust to changes in a single or a few pixels. These properties make the algorithm quite robust to different types of noise and other artifacts such as camera shake or image compression. Experiments illustrate the potential of the approach in several different applications such as change detection and feature matching.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129512527","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: 48

Animals on the Web 网络上的动物

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.57

Tamara L. Berg, D. Forsyth

引用次数: 204

Coupled Bayesian Framework for Dual Energy Image Registration 双能量图像配准的耦合贝叶斯框架

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.93

Hao Wu, Yunqiang Chen, T. Fang

引用次数: 3

Landmark-Based Geodesic Computation for Heuristically Driven Path Planning 启发式路径规划中基于地标的测地线计算

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.163

G. Peyré, L. Cohen

引用次数: 13