Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001最新文献_第8页

Modelling faces dynamically across views and over time 建模在视图和时间上是动态的

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937565

Yongmin Li, S. Gong, H. Liddell

引用次数: 62

Statistical context priming for object detection 用于对象检测的统计上下文启动

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937604

A. Torralba, P. Sinha

引用次数: 205

A framework for segmentation of talk and game shows 一个谈话和游戏节目分割的框架

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937671

O. Javed, Z. Rasheed, M. Shah

{"title":"A framework for segmentation of talk and game shows","authors":"O. Javed, Z. Rasheed, M. Shah","doi":"10.1109/ICCV.2001.937671","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937671","url":null,"abstract":"In this paper, we present a method to remove commercials from talk and game show videos and to segment these videos into host and guest shots. In our approach, we mainly rely on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize the inherent differences in scene structure of commercials and talk shows to differentiate between them. Similarly, we make use of the well-defined structure of talk shows, which can be exploited to classify shots as host or guest shots. The entire show is first segmented into camera shots based on color histogram. Then, we construct a data-structure (shot connectivity graph) which links similar shots over time. Analysis of the shot connectivity graph helps us to automatically separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial. Further analysis on stories is done to distinguish shots of the hosts from shots of the guests. We have tested our approach on several full-length shows (including commercials) and have achieved video segmentation with high accuracy. The whole scheme is fast and works even on low quality video (160/spl times/120 pixel images at 5 Hz).","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"8 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":"134552263","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: 23

Sparse PCA. Extracting multi-scale structure from data 稀疏主成分分析。从数据中提取多尺度结构

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937579

C. Chennubhotla, A. Jepson

引用次数: 49

Learning low dimensional invariant signature of 3-D object under varying view and illumination from 2-D appearances 学习二维物体在不同视角和光照下的低维不变特征

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937578

S. Li, Jie Yan, Xinwen Hou, ZeYu Li, HongJiang Zhang

{"title":"Learning low dimensional invariant signature of 3-D object under varying view and illumination from 2-D appearances","authors":"S. Li, Jie Yan, Xinwen Hou, ZeYu Li, HongJiang Zhang","doi":"10.1109/ICCV.2001.937578","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937578","url":null,"abstract":"In this paper, we propose an invariant signature representation for appearances of 3-D object under varying view and illumination, and a method for learning the signature from multi-view appearance examples. The signature, a nonlinear feature, provides a good basis for 3-D object detection and pose estimation due to its following properties. (I) Its location in the signature feature space is a simple function of the view and is insensitive or invariant to illumination. (2) It changes continuously as the view changes, so that the object appearances at all possible views should constitute a known simple curve segment (manifold) in the feature space. (3) The coordinates of rite object appearances in the feature space are correlated in a known way according to a predefined function of the view. The first two properties provide a basis for object detection and the third for view (pose) estimation. To compute the signature representation from input, we present a nonlinear regression method for learning a nonlinear mapping from the input (e.g. image) space to the feature space. The ideas of the signature representation and the learning method are illustrated with experimental results for the object of human face. It is shown that the face object can be effectively, modeled compactly in a 10-D nonlinear feature space. The 10-D signature presents excellent insensitivity to changes in illumination for any view. The correlation of the signature coordinates is well determined by the predefined parametric function. Applications of the proposed method in face detection and pose estimation are demonstrated.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"38 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":"134323346","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: 14

Cloning your own face with a desktop camera 用桌面相机克隆你自己的脸

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937707

Zhengyou Zhang, Zicheng Liu, Dennis Adler, Michael F. Cohen, E. Hanson, Ying Shan

引用次数: 2

3D object tracking using shape-encoded particle propagation 使用形状编码粒子传播的3D物体跟踪

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937641

H. Moon, R. Chellappa, A. Rosenfeld

{"title":"3D object tracking using shape-encoded particle propagation","authors":"H. Moon, R. Chellappa, A. Rosenfeld","doi":"10.1109/ICCV.2001.937641","DOIUrl":"https://doi.org/10.1109/ICCV.2001.937641","url":null,"abstract":"We present a comprehensive treatment of 3D object tracking by posing it as a nonlinear state estimation problem. The measurements are derived using the outputs of shape-encoded filters. The nonlinear state estimation is performed by solving the Zakai equation, and we use the branching particle propagation method for computing the solution. The unnormalized conditional density for the solution to the Zakai equation is realized by the weight of the particle. We first sample a set of particles approximating the initial distribution of the state vector conditioned on the observations, where each particle encodes the set of geometric parameters of the object. The weight of the particle represents geometric and temporal fit, which is computed bottom-up from the raw image using a shape-encoded filter. The particles branch so that the mean number of offspring is proportional to the weight. Time update is handled by employing a second-order motion model, combined with local stochastic search to minimize the prediction error. The prediction adjustment suggested by system identification theory is empirically verified to contribute to global stability. The amount of diffusion is effectively adjusted using a Kalman updating of the covariance matrix. WE have successfully applied this method to human head tracking, where we estimate head motion and compute structure using simple head and facial feature models.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"68 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":"122296461","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: 37

The KGBR viewpoint-lighting ambiguity and its resolution by generic constraints KGBR视点照明歧义及其通用约束解法

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937650

A. Yuille, J. Coughlan, S. Konishi

引用次数: 6

Euclidean reconstruction and auto-calibration from continuous motion 欧几里得重建和自校准连续运动

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937677

Fredrik Kahl, A. Heyden

引用次数: 21

Caustics of catadioptric cameras 反射光相机的焦散

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Pub Date : 2001-07-07 DOI: 10.1109/ICCV.2001.937581

R. Swaminathan, M. Grossberg, S. Nayar

引用次数: 127