Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)最新文献_第9页

Computing rectifying homographies for stereo vision 计算校正立体视觉的同形词

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786928

Charles T. Loop, Zhengyou Zhang

引用次数: 444

Extracting nonrigid motion and 3D structure of hurricanes from satellite image sequences without correspondences 从无对应的卫星图像序列中提取飓风的非刚体运动和三维结构

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784643

Lin Zhou, C. Kambhamettu, Dmitry Goldgof

{"title":"Extracting nonrigid motion and 3D structure of hurricanes from satellite image sequences without correspondences","authors":"Lin Zhou, C. Kambhamettu, Dmitry Goldgof","doi":"10.1109/CVPR.1999.784643","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784643","url":null,"abstract":"Image sequences capturing Hurricane Luis through meteorological satellites (GOES-8 and GOES-9) are used to estimate hurricane-top heights (structure) and hurricane winds (motion). This problem is difficult not only due to the absence of correspondence but also due to the lack of depth cues in the 2D hurricane images (scaled orthographic projection). In this paper, we present a structure and motion analysis system, called SMAS. In this system, the hurricane images are first segmented into small square areas. We assume that each small area is undergoing similar nonrigid motion. A suitable nonrigid motion model for cloud motion is first defined. Then, non-linear least-square method is used to fit the nonrigid motion model for each area in order to estimate the structure, motion model, and 3D nonrigid motion correspondences. Finally, the recovered hurricane-top heights and winds are presented along with an error analysis. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging, and have many potential applications in earth and space sciences, especially in cloud models for weather prediction.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76850843","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: 31

Generic object detection using model based segmentation 基于模型分割的通用目标检测

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784716

Zhiqian Wang, J. Ben-Arie

{"title":"Generic object detection using model based segmentation","authors":"Zhiqian Wang, J. Ben-Arie","doi":"10.1109/CVPR.1999.784716","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784716","url":null,"abstract":"This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that generic objects that are man made, frequently have surfaces which closely resemble standard model shapes such as rectangles, semi-circles etc. Due to the perspective transformations of optical imaging systems, a model shape may appear differently in the image with various orientations and aspect ratios. The set of possible appearances can be represented compactly by a few vectorial eigenbases that are derived from a small set of model shapes which are affine transformed in a wide parameter range. Instead of regular boundary of standard models, we apply a vectorial boundary which improves robustness to noise, background clutter and partial occlusion. The detection of generic shapes is realized by detecting local peaks of a similarity measure between the image edge map and an eigenspace combined set of the appearances. At each local maxima, a fast search approach based on a novel representation by an angle space is employed to determine the best matching between models and the underlying subimage. We find that angular representation in multidimensional search corresponds better to Euclidean distance than conventional projection and yields improved classification of noisy shapes. Experiments are performed in various interfering distortions, and robust detection and segmentation are achieved.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73784272","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: 9

Explanation-based facial motion tracking using a piecewise Bezier volume deformation model 基于解释的基于分段贝塞尔体变形模型的面部运动跟踪

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.787002

Hai Tao, Thomas S. Huang

{"title":"Explanation-based facial motion tracking using a piecewise Bezier volume deformation model","authors":"Hai Tao, Thomas S. Huang","doi":"10.1109/CVPR.1999.787002","DOIUrl":"https://doi.org/10.1109/CVPR.1999.787002","url":null,"abstract":"Capturing real motions from video sequences is a powerful method for automatic building of facial articulation models. In this paper, we propose an explanation-based facial motion tracking algorithm based on a piecewise Bezier volume deformation model (PBVD). The PBVD is a suitable model both for the synthesis and the analysis of facial images. It is linear and independent of the facial mesh structure. With this model, basic facial movements, or action units, are interactively defined. By changing the magnitudes of these action units, animated facial images are generated. The magnitudes of these action units can also be computed from real video sequences using a model-based tracking algorithm. However, in order to customize the articulation model for a particular face, the predefined PBVD action units need to be adaptively modified. In this paper, we first briefly introduce the PBVD model and its application in facial animation. Then a multiresolution PBVD-based motion tracking algorithm is presented. Finally, we describe an explanation-based tracking algorithm that takes the predefined action units as the initial articulation model and adaptively improves them during the tracking process to obtain a more realistic articulation model. Experimental results on PBVD-based animation, model-based tracking, and explanation-based tracking are shown in this paper.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75580711","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: 117

Material classification for 3D objects in aerial hyperspectral images 航空高光谱图像中三维物体的材料分类

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784641

D. Slater, G. Healey

{"title":"Material classification for 3D objects in aerial hyperspectral images","authors":"D. Slater, G. Healey","doi":"10.1109/CVPR.1999.784641","DOIUrl":"https://doi.org/10.1109/CVPR.1999.784641","url":null,"abstract":"Automated material classification from airborne imagery is an important capability for many applications including target recognition and geospatial database construction. Hyperspectral imagery provides a rich source of information for this purpose but utilization is complicated by the variability in a material's observed spectral signature due to the ambient conditions and the scene geometry. In this paper, we present a method that uses a single spectral radiance function measured from a material under unknown conditions to synthesize a comprehensive set of radiance spectra that corresponds to that material over a wide range of conditions. This set of radiance spectra can be used to build a hyperspectral subspace representation that can be used for material identification over a wide range of circumstances. We demonstrate the use of these algorithms for model synthesis and material mapping using HYDICE imagery acquired at Fort Hood, Texas. The method correctly maps several classes of roofing materials, roads, and vegetation over significant spectral changes due to variation in surface orientation. We show that the approach outperforms methods based on direct spectral comparison.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73232609","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: 27

Explaining optical flow events with parameterized spatio-temporal models 用参数化时空模型解释光流事件

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786959

Michael J. Black

引用次数: 68

Implicit representation and scene reconstruction from probability density functions 基于概率密度函数的隐式表示和场景重建

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784604

S. Seitz, P. Anandan

引用次数: 16

Efficient techniques for wide-angle stereo vision using surface projection models 利用表面投影模型实现广角立体视觉的高效技术

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786926

Philip W. Smith, Keith B. Johnson, M. Abidi

引用次数: 11

Bayesian multi-camera surveillance 贝叶斯多摄像头监控

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.784638

Vera M. Kettnaker, R. Zabih

引用次数: 338

Detection and removal of line scratches in motion picture films 电影胶片中线条划痕的检测与去除

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI: 10.1109/CVPR.1999.786991

L. Joyeux, Olivier Buisson, B. Besserer, S. Boukir

引用次数: 121