Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001最新文献_第2页

Noise in bilinear problems 双线性问题中的噪声

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937684

J. Haddon, D. Forsyth

引用次数: 5

Multi-agent event recognition 多智能体事件识别

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937608

Somboon Hongeng, R. Nevatia

引用次数: 212

Accurate catadioptric calibration for real-time pose estimation in room-size environments 在房间大小的环境中进行实时姿态估计的精确反射校准

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937508

Daniel G. Aliaga

引用次数: 98

Gradient vector flow fast geodesic active contours 梯度矢量流快速测地线活动轮廓

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937500

N. Paragios, O. Mellina-Gottardo, Visvanathan Ramesh

引用次数: 115

Physics-based model acquisition and identification in airborne spectral images 航空光谱图像中基于物理的模型获取与识别

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937633

D. Slater, G. Healey

引用次数: 9

Occlusion robust adaptive template tracking 遮挡鲁棒自适应模板跟踪

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937587

H. Nguyen, M. Worring, R. V. D. Boomgaard

引用次数: 135

A data-driven model for monocular face tracking 基于数据驱动的单目人脸跟踪模型

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937695

Salih Burak Göktürk, J. Bouguet, R. Grzeszczuk

{"title":"A data-driven model for monocular face tracking","authors":"Salih Burak Göktürk, J. Bouguet, R. Grzeszczuk","doi":"10.1109/ICCV.2001.937695","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937695","url":null,"abstract":"This paper describes a two-stage system for 3D tracking of pose and deformation of the human face in monocular image sequences without the use of special markers. The first stage of the system learns the space of all possible facial deformations by applying principal component analysis on real stereo tracking data. The resulting model approximates any generic shape as a linear combination of shape basis vectors. The second stage of the system uses this low-complexity deformable model for simultaneous tracking of pose and deformation of the face from a single image sequence. This stage is known as model-based monocular tracking. There are three main contributions of this paper. First we demonstrate that a data-driven approach for model construction is suitable for tracking non rigid objects and offers an elegant and practical alternative to the task of manual construction of models using 3D scanners or CAD modelers. Second, we show that such a method exhibits good tracking accuracy (errors less than 5 mm) and robustness characteristics. Third, we demonstrate that our system exhibits very promising generalization properties in enabling tracking of multiple persons with the same 3D model.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"2 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":"131809839","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: 82

Indexing based on scale invariant interest points 基于尺度不变兴趣点的索引

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937561

K. Mikolajczyk, C. Schmid

{"title":"Indexing based on scale invariant interest points","authors":"K. Mikolajczyk, C. Schmid","doi":"10.1109/ICCV.2001.937561","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937561","url":null,"abstract":"This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: (1) Interest points can be adapted to scale and give repeatable results (geometrically stable). (2) Local extrema over scale of normalized derivatives indicate the presence of characteristic local structures. Our method first computes a multi-scale representation for the Harris interest point detector. We then select points at which a local measure (the Laplacian) is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. These points are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. For indexing, the image is characterized by a set of scale invariant points; the scale associated with each point allows the computation of a scale invariant descriptor. Our descriptors are, in addition, invariant to image rotation, of affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"2 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":"133436831","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: 1357

4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities 4-传感器相机校准图像表示不变的阴影，阴影，照明和镜面

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937663

G. Finlayson, M. S. Drew

{"title":"4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities","authors":"G. Finlayson, M. S. Drew","doi":"10.1109/ICCV.2001.937663","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937663","url":null,"abstract":"Most lighting can be accurately modeled using a simplified Planckian function. If we form logarithms of color ratios of camera sensor values, then in a Lambertian plus specular two-lobe model of reflection the temperature-dependent term is separate and is seen as a straight line: i.e., changing lighting amounts to changing each pixel value in a straight line, for a given camera. Here we use a 4-sensor camera. In this case, forming color ratios reduces the dimensionality to 3. Applying logarithms and projecting onto the plane in the 3D color space orthogonal to the light-change direction results in an image representation that is invariant to illumination change. For a given camera, the position of the specular point in the 2D plane is always the same, independent of the lighting. Thus a camera calibration produces illumination invariance at a single pixel. In the plane, matte surfaces reduce to points and specularities are almost straight lines. Extending each pixel value back to the matte position, postulated to be the maximum radius from the fixed specular point, at any angle in the 2D plane, removes specularity. Thus images are independent of shading (by forming ratios), independent of shadows (by making them independent of illumination temperature) and independent of specularities. The method is examined by forming 4D images from hyperspectral images, using real camera sensors, with encouraging results.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"2 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":"131654182","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: 64

Omni-rig: linear self-recalibration of a rig with varying internal and external parameters 全方位钻机:线性自校准钻机与变化的内部和外部参数

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937509

A. Zomet, Lior Wolf, A. Shashua

引用次数: 21