Proceedings Ninth IEEE International Conference on Computer Vision最新文献_第3页

Surface reflectance modeling of real objects with interreflections 具有互反射的真实物体表面反射率建模

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238335

Takashi Machida, N. Yokoya, H. Takemura

引用次数: 24

Modeling textured motion : particle, wave and sketch 建模纹理运动:粒子，波浪和草图

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238343

Yizhou Wang, Song-Chun Zhu

{"title":"Modeling textured motion : particle, wave and sketch","authors":"Yizhou Wang, Song-Chun Zhu","doi":"10.1109/ICCV.2003.1238343","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238343","url":null,"abstract":"We present a generative model for textured motion phenomena, such as falling snow, wavy river and dancing grass, etc. Firstly, we represent an image as a linear superposition of image bases selected from a generic and over-complete dictionary. The dictionary contains Gabor bases for point/particle elements and Fourier bases for wave-elements. These bases compete to explain the input images. The transform from a raw image to a base or a token representation leads to large dimension reduction. Secondly, we introduce a unified motion equation to characterize the motion of these bases and the interactions between waves and particles, e.g. a ball floating on water. We use statistical learning algorithm to identify the structure of moving objects and their trajectories automatically. Then novel sequences can be synthesized easily from the motion and image models. Thirdly, we replace the dictionary of Gabor and Fourier bases with symbolic sketches (also bases). With the same image and motion model, we can render realistic and stylish cartoon animation. In our view, cartoon and sketch are symbolic visualization of the inner representation for visual perception. The success of the cartoon animation, in turn, suggests that our image and motion models capture the essence of visual perception of textured motion.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116042288","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: 53

Calibrating pan-tilt cameras in wide-area surveillance networks 广域监控网络中平移摄像机的标定

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238329

James Davis, Xing Chen

引用次数: 106

Eye gaze estimation from a single image of one eye 基于单眼图像的眼睛注视估计

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238328

Jian-Gang Wang, E. Sung, R. Venkateswarlu

引用次数: 195

Minimally-supervised classification using multiple observation sets 使用多个观测集的最小监督分类

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238358

C. Stauffer

{"title":"Minimally-supervised classification using multiple observation sets","authors":"C. Stauffer","doi":"10.1109/ICCV.2003.1238358","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238358","url":null,"abstract":"We discuss building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or object. When data can be measured by observation, it is often plentiful and it is often possible to make more than one observation of the state of a process or object. We discuss how to exploit the variability across such sets of observations of the same object to estimate class labels for unlabeled examples given a minimal number of labeled examples. In contrast to similar semisupervised classification procedures that define the likelihood that two observations share a label as a function of the embedded distance between the two observations, this method uses the Naive Bayes estimate of how often the two observations did result from the same observed process. Exploiting this additional source of information in an iterative estimation procedure can generalize complex classification models from single labeled observations. Some examples involving classification of tracked objects in a low-dimensional feature space given thousands of unlabeled observation sets are used to illustrate the effectiveness of this method.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244228","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: 13

Using specularities for recognition 利用镜面进行识别

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238669

Margarita Osadchy, D. Jacobs, R. Ramamoorthi

引用次数: 62

3D tracking = classification + interpolation 3D跟踪=分类+插值

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238659

Carlo Tomasi, Slav Petrov, A. Sastry

引用次数: 113

Maintaining multimodality through mixture tracking 通过混合跟踪维持多模态

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238473

J. Vermaak, A. Doucet, P. Pérez

引用次数: 463

Bayesian clustering of optical flow fields 光流场的贝叶斯聚类

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238470

J. Hoey, J. Little

引用次数: 17

Computing MAP trajectories by representing, propagating and combining PDFs over groups 通过在组上表示、传播和组合pdf来计算MAP轨迹

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI: 10.1109/ICCV.2003.1238637

Paul Smith, T. Drummond, K. Roussopoulos

{"title":"Computing MAP trajectories by representing, propagating and combining PDFs over groups","authors":"Paul Smith, T. Drummond, K. Roussopoulos","doi":"10.1109/ICCV.2003.1238637","DOIUrl":"https://doi.org/10.1109/ICCV.2003.1238637","url":null,"abstract":"This paper addresses the problem of computing the trajectory of a camera from sparse positional measurements that have been obtained from visual localisation, and dense differential measurements from odometry or inertial sensors. A fast method is presented for fusing these two sources of information to obtain the maximum a posteriori estimate of the trajectory. A formalism is introduced for representing probability density functions over Euclidean transformations, and it is shown how these density functions can be propagated along the data sequence and how multiple estimates of a transformation can be combined. A three-pass algorithm is described which makes use of these results to yield the trajectory of the camera. Simulation results are presented which are validated against a physical analogue of the vision problem, and results are then shown from sequences of approximately 1,800 frames captured from a video camera mounted on a go-kart. Several of these frames are processed using computer vision to obtain estimates of the position of the go-kart. The algorithm fuses these estimates with odometry from the entire sequence in 150 ms to obtain the trajectory of the kart.","PeriodicalId":131580,"journal":{"name":"Proceedings Ninth IEEE International Conference on Computer Vision","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129176104","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: 32