2011 International Conference on Computer Vision最新文献_第8页

Learning to cluster using high order graphical models with latent variables 学习使用具有潜在变量的高阶图形模型聚类

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126227

N. Komodakis

引用次数: 13

Building a better probabilistic model of images by factorization 用因子分解法建立更好的图像概率模型

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126473

B. J. Culpepper, Jascha Narain Sohl-Dickstein, B. Olshausen

引用次数: 14

Image based detection of geometric changes in urban environments 基于图像的城市环境几何变化检测

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126515

Aparna Taneja, Luca Ballan, M. Pollefeys

引用次数: 89

Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance 小鸟:使用体积原语和姿势标准化外观进行从属分类

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126238

Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu, Trevor Darrell, L. Davis

{"title":"Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance","authors":"Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu, Trevor Darrell, L. Davis","doi":"10.1109/ICCV.2011.6126238","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126238","url":null,"abstract":"Subordinate-level categorization typically rests on establishing salient distinctions between part-level characteristics of objects, in contrast to basic-level categorization, where the presence or absence of parts is determinative. We develop an approach for subordinate categorization in vision, focusing on an avian domain due to the fine-grained structure of the category taxonomy for this domain. We explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Training pose detectors requires a relatively large amount of training data per category when done from scratch; using a subordinate-level approach, we exploit a pose classifier trained at the basic-level, and extract part appearance and shape information to build subordinate-level models. Our model associates the underlying image pattern parameters used for detection with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization from relatively few training examples.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78492824","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: 217

Stereo reconstruction using high order likelihood 利用高阶似然进行立体重建

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126371

H. Jung, Kyoung Mu Lee, Sang Uk Lee

引用次数: 11

N-best maximal decoders for part models 零件模型的n -最佳最大解码器

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126552

Dennis Park, Deva Ramanan

引用次数: 126

Dynamic Manifold Warping for view invariant action recognition 动态流形翘曲的视图不变动作识别

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126290

Dian Gong, G. Medioni

引用次数: 91

Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces 基于拉普拉斯-贝尔特拉米节点分割和表面固有测地线曲率流的自动胼胝体提取

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126476

Rongjie Lai, Yonggang Shi, N. Sicotte, A. Toga

{"title":"Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces","authors":"Rongjie Lai, Yonggang Shi, N. Sicotte, A. Toga","doi":"10.1109/ICCV.2011.6126476","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126476","url":null,"abstract":"Corpus callosum (CC) is an important structure in human brain anatomy. In this work, we propose a fully automated and robust approach to extract corpus callosum from T1-weighted structural MR images. The novelty of our method is composed of two key steps. In the first step, we find an initial guess for the curve representation of CC by using the zero level set of the first nontrivial Laplace-Beltrami (LB) eigenfunction on the white matter surface. In the second step, the initial curve is deformed toward the final solution with a geodesic curvature flow on the white matter surface. For numerical solution of the geodesic curvature flow on surfaces, we represent the contour implicitly on a triangular mesh and develop efficient numerical schemes based on finite element method. Because our method depends only on the intrinsic geometry of the white matter surface, it is robust to orientation differences of the brain across population. In our experiments, we validate the proposed algorithm on 32 brains from a clinical study of multiple sclerosis disease and demonstrate that the accuracy of our results.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76641952","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

Adaptive deconvolutional networks for mid and high level feature learning 用于中高级特征学习的自适应反卷积网络

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126474

Matthew D. Zeiler, Graham W. Taylor, R. Fergus

引用次数: 1182

Exploiting the Manhattan-world assumption for extrinsic self-calibration of multi-modal sensor networks 利用曼哈顿世界假设进行多模态传感器网络的外部自定标

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126337

Marcel Brückner, Joachim Denzler

{"title":"Exploiting the Manhattan-world assumption for extrinsic self-calibration of multi-modal sensor networks","authors":"Marcel Brückner, Joachim Denzler","doi":"10.1109/ICCV.2011.6126337","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126337","url":null,"abstract":"Many new applications are enabled by combining a multi-camera system with a Time-of-Flight (ToF) camera, which is able to simultaneously record intensity and depth images. Classical approaches for self-calibration of a multi-camera system fail to calibrate such a system due to the very different image modalities. In addition, the typical environments of multi-camera systems are man-made and consist primary of only low textured objects. However, at the same time they satisfy the Manhattan-world assumption. We formulate the multi-modal sensor network calibration as a Maximum a Posteriori (MAP) problem and solve it by minimizing the corresponding energy function. First we estimate two separate 3D reconstructions of the environment: one using the pan-tilt unit mounted ToF camera and one using the multi-camera system. We exploit the Manhattan-world assumption and estimate multiple initial calibration hypotheses by registering the three dominant orientations of planes. These hypotheses are used as prior knowledge of a subsequent MAP estimation aiming to align edges that are parallel to these dominant directions. To our knowledge, this is the first self-calibration approach that is able to calibrate a ToF camera with a multi-camera system. Quantitative experiments on real data demonstrate the high accuracy of our approach.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73749958","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: 0