2011 International Conference on Computer Vision最新文献_第7页

The NBNN kernel NBNN内核

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126449

T. Tuytelaars, Mario Fritz, Kate Saenko, Trevor Darrell

引用次数: 124

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":"401 1","pages":"2034-2040"},"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

Fast articulated motion tracking using a sums of Gaussians body model 基于高斯和体模型的快速关节运动跟踪

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126338

Carsten Stoll, N. Hasler, Juergen Gall, H. Seidel, C. Theobalt

引用次数: 220

Assessing the aesthetic quality of photographs using generic image descriptors 使用通用图像描述符评估照片的美学质量

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126444

L. Marchesotti, F. Perronnin, Diane Larlus, G. Csurka

引用次数: 388

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

Recognizing jumbled images: The role of local and global information in image classification 混杂图像识别:局部和全局信息在图像分类中的作用

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126283

Devi Parikh

{"title":"Recognizing jumbled images: The role of local and global information in image classification","authors":"Devi Parikh","doi":"10.1109/ICCV.2011.6126283","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126283","url":null,"abstract":"The performance of current state-of-the-art computer vision algorithms at image classification falls significantly short as compared to human abilities. To reduce this gap, it is important for the community to know what problems to solve, and not just how to solve them. Towards this goal, via the use of jumbled images, we strip apart two widely investigated aspects: local and global information in images, and identify the performance bottleneck. Interestingly, humans have been shown to reliably recognize jumbled images. The goal of our paper is to determine a functional model that mimics how humans recognize jumbled images i.e. exploit local information alone, and further evaluate if existing implementations of this computational model suffice to match human performance. Surprisingly, in our series of human studies and machine experiments, we find that a simple bag-of-words based majority-vote-like strategy is an accurate functional model of how humans recognize jumbled images. Moreover, a straightforward machine implementation of this model achieves accuracies similar to human subjects at classifying jumbled images. This indicates that perhaps existing machine vision techniques already leverage local information from images effectively, and future research efforts should be focused on more advanced modeling of global information.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"196 1","pages":"519-526"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79855633","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: 45

Globally optimal solution to multi-object tracking with merged measurements 融合测量的多目标跟踪全局最优解

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126532

João F. Henriques, Rui Caseiro, Jorge P. Batista

引用次数: 145

Compact correlation coding for visual object categorization 用于视觉对象分类的紧凑相关编码

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126425

Nobuyuki Morioka, S. Satoh

{"title":"Compact correlation coding for visual object categorization","authors":"Nobuyuki Morioka, S. Satoh","doi":"10.1109/ICCV.2011.6126425","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126425","url":null,"abstract":"Spatial relationships between local features are thought to play a vital role in representing object categories. However, learning a compact set of higher-order spatial features based on visual words, e.g., doublets and triplets, remains a challenging problem as possible combinations of visual words grow exponentially. While the local pairwise codebook achieves a compact codebook of pairs of spatially close local features without feature selection, its formulation is not scale invariant and is only suitable for densely sampled local features. In contrast, the proximity distribution kernel is a scale-invariant and robust representation capturing rich spatial proximity information between local features, but its representation grows quadratically in the number of visual words. Inspired by the two abovementioned techniques, this paper presents the compact correlation coding that combines the strengths of the two. Our method achieves a compact representation that is scaleinvariant and robust against object deformation. In addition, we adopt sparse coding instead of k-means clustering during the codebook construction to increase the discriminative power of our method. We systematically evaluate our method against both the local pairwise codebook and proximity distribution kernel on several challenging object categorization datasets to show performance improvements.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"146 1","pages":"1639-1646"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80541833","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: 21

Linear dependency modeling for feature fusion 特征融合的线性依赖建模

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126477

A. J. Ma, P. Yuen

引用次数: 15