2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第3页

Computing iconic summaries of general visual concepts 计算一般视觉概念的图标摘要

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562959

R. Raguram, S. Lazebnik

引用次数: 62

Incremental estimation without specifying a-priori covariance matrices for the novel parameters 增量估计不指定先验协方差矩阵的新参数

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563139

C. Beder, Richard Steffen

{"title":"Incremental estimation without specifying a-priori covariance matrices for the novel parameters","authors":"C. Beder, Richard Steffen","doi":"10.1109/CVPRW.2008.4563139","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563139","url":null,"abstract":"We will present a novel incremental algorithm for the task of online least-squares estimation. Our approach aims at combining the accuracy of least-squares estimation and the fast computation of recursive estimation techniques like the Kalman filter. Analyzing the structure of least-squares estimation we devise a novel incremental algorithm, which is able to introduce new unknown parameters and observations into an estimation simultaneously and is equivalent to the optimal overall estimation in case of linear models. It constitutes a direct generalization of the well-known Kalman filter allowing to augment the state vector inside the update step. In contrast to classical recursive estimation techniques no artificial initial covariance for the new unknown parameters is required here. We will show, how this new algorithm allows more flexible parameter estimation schemes especially in the case of scene and motion reconstruction from image sequences. Since optimality is not guaranteed in the non-linear case we will also compare our incremental estimation scheme to the optimal bundle adjustment on a real image sequence. It will be shown that competitive results are achievable using the proposed technique.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124276834","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

Multi-parts and multi-feature fusion in face verification 人脸验证中的多部位多特征融合

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563107

Yan Xiang, G. Su

引用次数: 18

Internet video category recognition 互联网视频分类识别

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562960

Grant Schindler, Larry Zitnick, Matthew A. Brown

引用次数: 35

Efficient anisotropic α-Kernels decompositions and flows 高效各向异性α-核分解与流动

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562982

Micha Feigin-Almon, N. Sochen, B. Vemuri

引用次数: 2

Autonomous navigation and mapping using monocular low-resolution grayscale vision 使用单眼低分辨率灰度视觉的自主导航和制图

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563136

Vidya N. Murali, Stan Birchfield

{"title":"Autonomous navigation and mapping using monocular low-resolution grayscale vision","authors":"Vidya N. Murali, Stan Birchfield","doi":"10.1109/CVPRW.2008.4563136","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563136","url":null,"abstract":"An algorithm is proposed to answer the challenges of autonomous corridor navigation and mapping by a mobile robot equipped with a single forward-facing camera. Using a combination of corridor ceiling lights, visual homing, and entropy, the robot is able to perform straight line navigation down the center of an unknown corridor. Turning at the end of a corridor is accomplished using Jeffrey divergence and time-to-collision, while deflection from dead ends and blank walls uses a scalar entropy measure of the entire image. When combined, these metrics allow the robot to navigate in both textured and untextured environments. The robot can autonomously explore an unknown indoor environment, recovering from difficult situations like corners, blank walls, and initial heading toward a wall. While exploring, the algorithm constructs a Voronoi-based topo-geometric map with nodes representing distinctive places like doors, water fountains, and other corridors. Because the algorithm is based entirely upon low-resolution (32 times 24) grayscale images, processing occurs at over 1000 frames per second.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":" 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120834117","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

Integrated segmentation and motion analysis of cardiac MR images using a subject-specific dynamical model 综合分割和运动分析的心脏磁共振图像使用特定主题的动态模型

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563007

Yun Zhu, X. Papademetris, A. Sinusas, J. Duncan

{"title":"Integrated segmentation and motion analysis of cardiac MR images using a subject-specific dynamical model","authors":"Yun Zhu, X. Papademetris, A. Sinusas, J. Duncan","doi":"10.1109/CVPRW.2008.4563007","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563007","url":null,"abstract":"In this paper we propose an integrated cardiac segmentation and motion tracking algorithm. First, we present a subject-specific dynamical model (SSDM) that simultaneously handles inter-subject variability and temporal dynamics (intra-subject variability), such that it can progressively identify the subject vector associated with a new cardiac sequence, and use this subject vector to predict the subject-specific segmentation of the future frames based on the shapes observed in earlier frames. Second, we use the segmentation as a guide in selecting feature points with significant shape characteristics, and invoke the generalized robust point matching (G-RPM) strategy with boundary element method (BEM)-based regularization model to estimate physically realistic displacement field in a computationally efficient way. The integrated algorithm is formulated in a recursive Bayesian framework that sequentially segments cardiac images and estimates myocardial displacements. ldquoLeave-one-outrdquo validation on 32 sequences demonstrates that the segmentation results are improved when the SSDM is used, and the tracking results are much more accurate when the segmentation module is added.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319264","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: 7

IVUS tissue characterization with sub-class error-correcting output codes IVUS组织表征与亚类纠错输出代码

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563021

Sergio Escalera, O. Pujol, J. Mauri, P. Radeva

引用次数: 5

Distributed segmentation and classification of human actions using a wearable motion sensor network 基于可穿戴运动传感器网络的人体动作分布式分割与分类

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563176

A. Yang, Sameer Iyengar, S. Sastry, R. Bajcsy, P. Kuryloski, R. Jafari

{"title":"Distributed segmentation and classification of human actions using a wearable motion sensor network","authors":"A. Yang, Sameer Iyengar, S. Sastry, R. Bajcsy, P. Kuryloski, R. Jafari","doi":"10.1109/CVPRW.2008.4563176","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563176","url":null,"abstract":"We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. We show that the distribution of multiple action classes satisfies a mixture subspace model, one sub-space for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the representation. We further provide fast linear solvers to compute such representation via l1-minimization. Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036868","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: 108

Detecting mirror-symmetry of a volumetric shape from its single 2D image 从单个二维图像中检测体积形状的镜像对称性

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562976

T. Sawada, Z. Pizlo

引用次数: 9