2009 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第8页

Noninvasive volumetric imaging of cardiac electrophysiology 心脏电生理的无创容积成像

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206717

Linwei Wang, Heye Zhang, Ken C. L. Wong, Huafeng Liu, P. Shi

引用次数: 8

A streaming framework for seamless building reconstruction from large-scale aerial LiDAR data 基于大规模航空激光雷达数据的无缝建筑重建流框架

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206760

Qian-Yi Zhou, U. Neumann

引用次数: 52

Image deblurring for less intrusive iris capture 图像去模糊，较少侵入虹膜捕获

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206700

Xinyu Huang, Liu Ren, Ruigang Yang

引用次数: 27

Marked point processes for crowd counting 人群计数的标记点过程

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206621

Weina Ge, R. Collins

引用次数: 263

Active volume models for 3D medical image segmentation 三维医学图像分割的活动体模型

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206563

Tian Shen, Hongsheng Li, Z. Qian, Xiaolei Huang

{"title":"Active volume models for 3D medical image segmentation","authors":"Tian Shen, Hongsheng Li, Z. Qian, Xiaolei Huang","doi":"10.1109/CVPR.2009.5206563","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206563","url":null,"abstract":"In this paper, we propose a novel predictive model for object boundary, which can integrate information from any sources. The model is a dynamic “object” model whose manifestation includes a deformable surface representing shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. Unlike Snakes, Level Set, Graph Cut, MRF and CRF approaches, the model is “self-contained” in that it does not model the background, but rather focuses on an accurate representation of the foreground object's attributes. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. The shape of the 3D model is considered as an elastic solid, with a simplex-mesh (i.e. finite element triangulation) surface made of thousands of vertices. Deformations of the model are derived from a linear system that encodes external forces from the boundary of a Region of Interest (ROI), which is a binary mask representing the object region predicted by the current model. Efficient optimization and fast convergence of the model are achieved using the Finite Element Method (FEM). Other advantages of the model include the ease of dealing with topology changes and its ability to incorporate human interactions. Segmentation and validation results are presented for experiments on noisy 3D medical images.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521024","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: 28

Discriminatively trained particle filters for complex multi-object tracking 用于复杂多目标跟踪的判别训练粒子滤波器

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206801

Robin Hess, Alan Fern

引用次数: 135

An instance selection approach to Multiple instance Learning 多实例学习的实例选择方法

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206655

Zhouyu Fu, A. Robles-Kelly

引用次数: 31

A family of contextual measures of similarity between distributions with application to image retrieval 分布间相似性的一组上下文度量及其在图像检索中的应用

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206505

F. Perronnin, Yan Liu, J. Renders

{"title":"A family of contextual measures of similarity between distributions with application to image retrieval","authors":"F. Perronnin, Yan Liu, J. Renders","doi":"10.1109/CVPR.2009.5206505","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206505","url":null,"abstract":"We introduce a novel family of contextual measures of similarity between distributions: the similarity between two distributions q and p is measured in the context of a third distribution u. In our framework any traditional measure of similarity / dissimilarity has its contextual counterpart. We show that for two important families of divergences (Bregman and Csisz'ar), the contextual similarity computation consists in solving a convex optimization problem. We focus on the case of multinomials and explain how to compute in practice the similarity for several well-known measures. These contextual measures are then applied to the image retrieval problem. In such a case, the context u is estimated from the neighbors of a query q. One of the main benefits of our approach lies in the fact that using different contexts, and especially contexts at multiple scales (i.e. broad and narrow contexts), provides different views on the same problem. Combining the different views can improve retrieval accuracy. We will show on two very different datasets (one of photographs, the other of document images) that the proposed measures have a relatively small positive impact on macro Average Precision (which measures purely ranking) and a large positive impact on micro Average Precision (which measures both ranking and consistency of the scores across multiple queries).","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123443148","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: 42

Discriminative subvolume search for efficient action detection 判别子卷搜索，有效的动作检测

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206671

Junsong Yuan, Zicheng Liu, Ying Wu

{"title":"Discriminative subvolume search for efficient action detection","authors":"Junsong Yuan, Zicheng Liu, Ying Wu","doi":"10.1109/CVPR.2009.5206671","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206671","url":null,"abstract":"Actions are spatio-temporal patterns which can be characterized by collections of spatio-temporal invariant features. Detection of actions is to find the re-occurrences (e.g. through pattern matching) of such spatio-temporal patterns. This paper addresses two critical issues in pattern matching-based action detection: (1) efficiency of pattern search in 3D videos and (2) tolerance of intra-pattern variations of actions. Our contributions are two-fold. First, we propose a discriminative pattern matching called naive-Bayes based mutual information maximization (NBMIM) for multi-class action categorization. It improves the state-of-the-art results on standard KTH dataset. Second, a novel search algorithm is proposed to locate the optimal subvolume in the 3D video space for efficient action detection. Our method is purely data-driven and does not rely on object detection, tracking or background subtraction. It can well handle the intra-pattern variations of actions such as scale and speed variations, and is insensitive to dynamic and clutter backgrounds and even partial occlusions. The experiments on versatile datasets including KTH and CMU action datasets demonstrate the effectiveness and efficiency of our method.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121698973","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: 317

Symmetry integrated region-based image segmentation 基于区域的对称集成图像分割

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206570

Yu Sun, B. Bhanu

引用次数: 11