2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)最新文献_第4页

A Visual Vocabulary for Flower Classification 花卉分类的视觉词汇

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.42

M. Nilsback, Andrew Zisserman

引用次数: 887

Kernel-based Template Alignment 基于内核的模板对齐

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.162

I. Guskov

引用次数: 21

Surface Geometric Constraints for Stereo in Belief Propagation 信念传播中的立体曲面几何约束

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.299

Gang Li, S. Zucker

引用次数: 48

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects 部分遮挡物体识别与分割的布局一致随机场

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.305

J. Winn, J. Shotton

引用次数: 318

Separation of Highlight Reflections on Textured Surfaces 纹理表面上高光反射的分离

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.273

P. Tan, Long Quan, Stephen Lin

引用次数: 58

Bilayer Segmentation of Live Video 实时视频的双层分割

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.69

A. Criminisi, G. Cross, A. Blake, V. Kolmogorov

{"title":"Bilayer Segmentation of Live Video","authors":"A. Criminisi, G. Cross, A. Blake, V. Kolmogorov","doi":"10.1109/CVPR.2006.69","DOIUrl":"https://doi.org/10.1109/CVPR.2006.69","url":null,"abstract":"This paper presents an algorithm capable of real-time separation of foreground from background in monocular video sequences. Automatic segmentation of layers from colour/contrast or from motion alone is known to be error-prone. Here motion, colour and contrast cues are probabilistically fused together with spatial and temporal priors to infer layers accurately and efficiently. Central to our algorithm is the fact that pixel velocities are not needed, thus removing the need for optical flow estimation, with its tendency to error and computational expense. Instead, an efficient motion vs nonmotion classifier is trained to operate directly and jointly on intensity-change and contrast. Its output is then fused with colour information. The prior on segmentation is represented by a second order, temporal, Hidden Markov Model, together with a spatial MRF favouring coherence except where contrast is high. Finally, accurate layer segmentation and explicit occlusion detection are efficiently achieved by binary graph cut. The segmentation accuracy of the proposed algorithm is quantitatively evaluated with respect to existing groundtruth data and found to be comparable to the accuracy of a state of the art stereo segmentation algorithm. Foreground/ background segmentation is demonstrated in the application of live background substitution and shown to generate convincingly good quality composite video.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"21 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":"134461925","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: 317

Estimating Intrinsic Component Images using Non-Linear Regression 用非线性回归估计图像的固有分量

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.114

M. Tappen, E. Adelson, W. Freeman

引用次数: 106

Pursuing Informative Projection on Grassmann Manifold 追求格拉斯曼流形上的信息投影

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.231

Dahua Lin, Shuicheng Yan, Xiaoou Tang

引用次数: 19

3D Building Detection and Modeling from Aerial LIDAR Data 基于航空激光雷达数据的三维建筑检测与建模

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.12

Vivek Verma, Rakesh Kumar, S. Hsu

{"title":"3D Building Detection and Modeling from Aerial LIDAR Data","authors":"Vivek Verma, Rakesh Kumar, S. Hsu","doi":"10.1109/CVPR.2006.12","DOIUrl":"https://doi.org/10.1109/CVPR.2006.12","url":null,"abstract":"This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"3 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":"133811670","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: 349

Segmentation by Level Sets and Symmetry 基于水平集和对称性的分割

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.270

Tammy Riklin-Raviv, N. Kiryati, N. Sochen

{"title":"Segmentation by Level Sets and Symmetry","authors":"Tammy Riklin-Raviv, N. Kiryati, N. Sochen","doi":"10.1109/CVPR.2006.270","DOIUrl":"https://doi.org/10.1109/CVPR.2006.270","url":null,"abstract":"Shape symmetry is an important cue for image understanding. In the absence of more detailed prior shape information, segmentation can be significantly facilitated by symmetry. However, when symmetry is distorted by perspectivity, the detection of symmetry becomes non-trivial, thus complicating symmetry-aided segmentation. We present an original approach for segmentation of symmetrical objects accommodating perspective distortion. The key idea is the use of the replicative form induced by the symmetry for challenging segmentation tasks. This is accomplished by dynamic extraction of the object boundaries, based on the image gradients, gray levels or colors, concurrently with registration of the image symmetrical counterpart (e.g. reflection) to itself. The symmetrical counterpart of the evolving object contour supports the segmentation by resolving possible ambiguities due to noise, clutter, distortion, shadows, occlusions and assimilation with the background. The symmetry constraint is integrated in a comprehensive level-set functional for segmentation that determines the evolution of the delineating contour. The proposed framework is exemplified on various images of skewsymmetrical objects and its superiority over state of the art variational segmentation techniques is demonstrated.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"94 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":"115765025","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: 45