Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)最新文献_第8页

Automatic hierarchical classification using time-based co-occurrences 使用基于时间的共现自动分层分类

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784654

C. Stauffer

引用次数: 36

3D deformable image matching using multiscale minimization of global energy functions 使用全局能量函数的多尺度最小化的三维可变形图像匹配

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784724

O. Musse, F. Heitz, J. Armspach

引用次数: 26

Recognition of strings using nonstationary Markovian models: an application in ZIP code recognition 使用非平稳马尔可夫模型的字符串识别:在邮政编码识别中的应用

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784626

D. Bouchaffra, Venu Govindaraju, S. Srihari

引用次数: 6

An integral formulation for differential photometric stereo 微分光度立体的积分公式

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786927

James J. Clark, H. Pekau

引用次数: 7

Background estimation and removal based on range and color 基于范围和颜色的背景估计和去除

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784721

G. Gordon, Trevor Darrell, M. Harville, J. Woodfill

{"title":"Background estimation and removal based on range and color","authors":"G. Gordon, Trevor Darrell, M. Harville, J. Woodfill","doi":"10.1109/CVPR.1999.784721","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784721","url":null,"abstract":"Background estimation and removal based on the joint use of range and color data produces superior results than can be achieved with either data source alone. This is increasingly relevant as inexpensive, real-time, passive range systems become more accessible through novel hardware and increased CPU processing speeds. Range is a powerful signal for segmentation which is largely independent of color and hence not effected by the classic color segmentation problems of shadows and objects with color similar to the background. However range alone is also not sufficient for the good segmentation: depth measurements are rarely available at all pixels in the scene, and foreground objects may be indistinguishable in depth when they are close to the background. Color segmentation is complementary in these cases. Surprisingly, little work has been done to date on joint range and color segmentation. We describe and demonstrate a background estimation method based on a multidimensional (range and color) clustering at each image pixel. Segmentation of the foreground in a given frame is performed via comparison with background statistics in range and normalized color. Important implementation issues such as treatment of shadows and low confidence measurements are discussed in detail.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"19 1","pages":"459-464 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91275415","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: 193

Non-metric calibration of wide-angle lenses and polycameras 广角镜头和多镜头的非公制校准

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784714

R. Swaminathan, S. Nayar

{"title":"Non-metric calibration of wide-angle lenses and polycameras","authors":"R. Swaminathan, S. Nayar","doi":"10.1109/CVPR.1999.784714","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784714","url":null,"abstract":"Images taken with wide-angle cameras tend to have severe distortions which pull points towards the optical center. This paper proposes a method for recovering the distortion parameters without the use of any calibration objects. The distortions cause straight lines in the scene to appear as curves in the image. Our algorithm seeks to find the distortion parameters that would map the image curves to straight lines. The user selects a small set of points along the image curves. Recovery of the parameters is formulated as the minimization of an objective function which is designed to explicitly account for noise in the selected image points. Experimental results are presented for synthetic data with different noise levels as well as for real images. Once calibrated, the image streams from these cameras can be undistorted in real time using look up tables. We also present an application of this calibration method for wide-angle camera clusters, which we call polycameras. We apply our distortion correction technique to a polycamera with four wide-angle cameras to create a high resolution 360 degree panorama in real-time.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"1 1","pages":"413-419 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89980184","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: 278

Estimating model parameters and boundaries by minimizing a joint, robust objective function 通过最小化联合鲁棒目标函数来估计模型参数和边界

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784710

C. Stewart, Kishore Bubna, A. Perera

引用次数: 10

The discriminatory power of ordinal measures - towards a new coefficient 序数措施的歧视性力量——朝向一个新的系数

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786920

S. Scherer, A. Pinz, P. Werth

引用次数: 23

Gesture localization and recognition using probabilistic visual learning 基于概率视觉学习的手势定位与识别

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784615

Raouf Hamdan, F. Heitz, L. Thoraval

{"title":"Gesture localization and recognition using probabilistic visual learning","authors":"Raouf Hamdan, F. Heitz, L. Thoraval","doi":"10.1109/CVPR.1999.784615","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784615","url":null,"abstract":"A generic approach for the extraction and recognition of gesture using raw grey-level images is presented. The probabilistic visual learning approach, a learning method recently proposed by B. Moghaddam and A. Pentland (1997), is used to create a set of compact statistical representations of gesture appearance on low dimensional eigenspaces. The same probabilistic modeling framework is used to extract and track gesture and to perform gesture recognition over long image sequences. Gesture extraction and tracking are based on maximum likelihood gesture detection in the input image. Recognition is performed by using the set of learned probabilistic appearance models as estimates of the emission probabilities of a continuous density hidden Markov model (CDHMM). Although the segmentation and CDHMM-based recognition use raw grey-level images, the method is fast, thanks to the data compression obtained by probabilistic visual learning. The approach is comprehensive and may be applied to other visual motion recognition tasks. It does not require application-tailored extraction of image features, the use of markers or gloves. A real-time implementation of the method on a standard PC-based vision system is under consideration.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":"25 1","pages":"98-103 Vol. 2"},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83253201","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: 20

Real-time periodic motion detection, analysis, and applications 实时周期性运动检测，分析和应用

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784652

Ross Cutler, L. Davis

引用次数: 76