Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001最新文献_第4页

Camera calibration and 3D reconstruction from single images using parallelepipeds 相机校准和三维重建从单幅图像使用平行六面体

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937510

Marta Wilczkowiak, Edmond Boyer, P. Sturm

{"title":"Camera calibration and 3D reconstruction from single images using parallelepipeds","authors":"Marta Wilczkowiak, Edmond Boyer, P. Sturm","doi":"10.1109/ICCV.2001.937510","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937510","url":null,"abstract":"In this paper parallelepipeds and their use in camera calibration and 3D reconstruction processes are studied. Parallelepipeds naturally characterize rigidity constraints present in a scene, such as parallelism and orthogonality. A subclass of parallelepipeds-the cuboids-has been frequently used over the past to partially calibrate cameras. However, the full potential of parallelepipeds, in camera calibration as well as in scene reconstruction, has never been clearly established. We propose a new framework for the use of parallelepipeds which is based on an extensive study of this potential. In particular, we exhibit the complete duality that exists between the intrinsic metric characteristics of a parallelepiped and the intrinsic parameters of a camera. Our framework allows to fully exploit parallelepipeds and thus overcomes several limitations of calibration approaches based on cuboids. To illustrate this framework, we present an original and very efficient interactive method for 3D reconstruction from single images. This method allows to quickly build a scene model from a single uncalibrated image.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371922","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: 136

Color constancy using KL-divergence 使用kl -散度的颜色常数

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937524

Charles R. Rosenberg, M. Hebert, S. Thrun

引用次数: 60

Learning local evidence for shading and reflectance 学习阴影和反射率的局部证据

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937585

Matt Bell, W. Freeman

{"title":"Learning local evidence for shading and reflectance","authors":"Matt Bell, W. Freeman","doi":"10.1109/ICCV.2001.937585","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937585","url":null,"abstract":"We address the important and unsolved problem of determining whether variations in image intensity are caused by changes in surface normal (shading) or reflectance (paint). A solution to this problem is necessary for machines to interpret images as people do and could have many applications. We take a learning-based approach. We generate a trainiiag set of synthetic images containing both surface normal and reflectance variations, and then label the variations at each position, scale, and orientation as to whether they are caused by shading or paint. The classification is done locally, using a feature vector of nonlinear filter responses. We fit a probability density model to the filter outputs using a mixture of factor analyzers. The resulting model indicates the probability based on local image evidence, that a pyramid coefficient at each orientation and scale is caused by shading or reflectance variations. Although the classification is done using a fixed lighting direction, we can solve for the correct lighting direction by rotating the image to the orientation, relative to the light source, that gives the most shape-like labelings. The labeling allows us to reconstruct two high passed images: one contains those parts of the input image caused by shading effects, while the other contains only those parts caused by reflectance changes. The resulting classifications compare well with human psychophysical performance on a test set of images, and show good results for test photographs.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126031600","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: 73

Demonstration of segmentation with interactive graph cuts 演示交互式图切割分割

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937703

Yuri Boykov, M. Jolly

{"title":"Demonstration of segmentation with interactive graph cuts","authors":"Yuri Boykov, M. Jolly","doi":"10.1109/ICCV.2001.937703","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937703","url":null,"abstract":"We demonstrate a new technique for general purpose interactive segmentation of N-dimensional images. The method creates two segments: “object” and “background”. The technical details can be found in our paper [1] in this proceedings. Below we concentrate on the actual interface. The user can enter seeds via mouse-operated brush of red (for object) or blue (for background) color. The size of the brush can be changed depending on the size of the object. The user should paint some pixels in the object of interest and some in the background. The seeds provide some clues on what the user intends to segment. As soon as initial seeds are entered, the whole image/volume can be segmented automatically. Basically, the algorithm tries to “predict” how the user would want to paint the rest of the image. Segmentation results are presented by highlighting the object and background segments with red and blue colors. Thus, the object segment appears reddish while the background appears bluish. This gives an intuitive feeling that the algorithm completes the painting started by the user. An optimal segmentation can be very efficiently recomputed when the user adds or removes any seeds. This allows the user to correct any result imperfections quickly via very intuitive interactions. If the algorithm makes a mistake, the user can add a stroke of red paint in the bluish segment (or blue paint in the reddish segment). The new segmentation would very quickly repaint the whole image to comply with additional hints from the user. Our method is not sensitive to exact positioning of seeds. Normally, the results would not change in the seeds are moved within the same object in the image or volume. Our method applies to N-D images (volumes). In case of 3D data the seeds are entered in selected representative slices. The information is automatically propagated between the slices because we compute our optimal segmentation directly in the volume. Thus, the whole volume can be segmented based on seeds in a single slice. 2. Examples","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116666108","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

Real-time virtual object insertion 实时虚拟对象插入

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937711

P. Favaro, Hailin Jin, Stefano Soatto

引用次数: 3

Geometrical fundamentals of polycentric panoramas 多中心全景的几何基础

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937566

F. Huang, Shou-Kang Wei, R. Klette

引用次数: 36

Linear multi view reconstruction and camera recovery 线性多视图重建和相机恢复

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937497

C. Rother, S. Carlsson

引用次数: 62

A variational model for filling-in gray level and color images 灰度和彩色图像填充的变分模型

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937493

C. Ballester, V. Caselles, J. Verdera, M. Bertalmío, G. Sapiro

引用次数: 80

JetStream: probabilistic contour extraction with particles JetStream:粒子概率轮廓提取

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937670

P. Pérez, A. Blake, Michel Gangnet

引用次数: 135

Color eigenflows: statistical modeling of joint color changes 颜色特征流:关节颜色变化的统计建模

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937574

Erik G. Miller, Kinh H. Tieu

引用次数: 35