2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)最新文献_第8页

Intelligent Collaborative Tracking by Mining Auxiliary Objects 基于辅助对象挖掘的智能协同跟踪

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.157

Ming Yang, Ying Wu, S. Lao

{"title":"Intelligent Collaborative Tracking by Mining Auxiliary Objects","authors":"Ming Yang, Ying Wu, S. Lao","doi":"10.1109/CVPR.2006.157","DOIUrl":"https://doi.org/10.1109/CVPR.2006.157","url":null,"abstract":"Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either computationally intensive with the use of sophisticated image observation models, or vulnerable to the false alarms. This greatly threatens long-duration robust tracking. This paper presents a novel solution to this dilemma by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties at least in a short time interval: (1) persistent co-occurrence with the target; (2) consistent motion correlation with the target; and (3) easy to track. The collaborative tracking of these auxiliary objects leads to an efficient computation as well as a strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114246516","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: 35

Structure and View Estimation for Tomographic Reconstruction: A Bayesian Approach 层析成像重建的结构和视图估计:贝叶斯方法

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.295

S. P. Mallick, Sameer Agarwal, D. Kriegman, Serge J. Belongie, B. Carragher, C. Potter

{"title":"Structure and View Estimation for Tomographic Reconstruction: A Bayesian Approach","authors":"S. P. Mallick, Sameer Agarwal, D. Kriegman, Serge J. Belongie, B. Carragher, C. Potter","doi":"10.1109/CVPR.2006.295","DOIUrl":"https://doi.org/10.1109/CVPR.2006.295","url":null,"abstract":"This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) - a widely used reconstruction tool in cryo-EM.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114601255","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: 30

Interactive Feature Tracking using K-D Trees and Dynamic Programming 基于K-D树和动态规划的交互式特征跟踪

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.158

Aeron Buchanan, A. Fitzgibbon

引用次数: 76

Graph Partitioning by Spectral Rounding: Applications in Image Segmentation and Clustering 基于谱舍入的图分割:在图像分割和聚类中的应用

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.129

David Tolliver, G. Miller

引用次数: 109

Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models 自底向上的,使用原始草图特征和图形模型的自上而下的对象检测

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.74

Iasonas Kokkinos, P. Maragos, A. Yuille

引用次数: 44

Discriminative Object Class Models of Appearance and Shape by Correlatons 基于相关性的外观和形状的判别对象类模型

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.102

S. Savarese, J. Winn, A. Criminisi

引用次数: 241

Acceleration Strategies for Gaussian Mean-Shift Image Segmentation 高斯均值移位图像分割的加速策略

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.44

M. A. Carreira-Perpiñán

引用次数: 88

Classifying Human Dynamics Without Contact Forces 没有接触力的人体动力学分类

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.75

A. Bissacco, Stefano Soatto

引用次数: 15

Improving Recognition of Novel Input with Similarity 利用相似度提高新输入的识别

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.151

Jerod J. Weinman, E. Learned-Miller

引用次数: 26

Diffusion Distance for Histogram Comparison 直方图比较的扩散距离

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Pub Date : 2006-06-17 DOI: 10.1109/CVPR.2006.99

Haibin Ling, K. Okada

{"title":"Diffusion Distance for Histogram Comparison","authors":"Haibin Ling, K. Okada","doi":"10.1109/CVPR.2006.99","DOIUrl":"https://doi.org/10.1109/CVPR.2006.99","url":null,"abstract":"In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.","PeriodicalId":421737,"journal":{"name":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129278327","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: 270