Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition最新文献_第10页

Detecting and Locating Crosswalks using a Camera Phone. 使用照相手机检测和定位人行横道。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPRW.2008.4563143

Volodymyr Ivanchenko, James Coughlan, Huiying Shen

引用次数: 71

Localized Statistics for DW-MRI Fiber Bundle Segmentation. DW-MRI纤维束分割的局部统计。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/cvprw.2008.4562999

Shawn Lankton, John Melonakos, James Malcolm, Samuel Dambreville, Allen Tannenbaum

引用次数: 14

Nonlinear Image Representation Using Divisive Normalization. 使用分裂归一化的非线性图像表示。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587821

Siwei Lyu, Eero P Simoncelli

引用次数: 164

A Novel Representation for Riemannian Analysis of Elastic Curves in ℝ 弹性曲线的黎曼分析的一种新表示

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2007-07-16 DOI: 10.1109/CVPR.2007.383185

Shantanu H Joshi, Eric Klassen, Anuj Srivastava, Ian Jermyn

引用次数: 206

Multiple Class Segmentation Using A Unified Framework over Mean-Shift Patches. 基于Mean-Shift补丁的统一框架多类分割。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2007-07-16 DOI: 10.1109/CVPR.2007.383229

Lin Yang, Peter Meer, David J Foran

引用次数: 135

Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves. 在用于曲线形状分析的方根弹性（SRE）框架中去除保形变换

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2007-01-01 DOI: 10.1007/978-3-540-74198-5_30

Shantanu H Joshi, Eric Klassen, Anuj Srivastava, Ian Jermyn

引用次数: 0

Diffeomorphic Matching of Diffusion Tensor Images. 扩散张量图像的差分匹配。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2006-07-05 DOI: 10.1109/CVPRW.2006.65

Yan Cao, Michael I Miller, Susumu Mori, Raimond L Winslow, Laurent Younes

引用次数: 0

Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence. 基于cdf的Jensen-Shannon散度的分组点模式配准。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2006-07-05 DOI: 10.1109/CVPR.2006.131

Fei Wang, Baba C Vemuri, Anand Rangarajan

{"title":"Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence.","authors":"Fei Wang, Baba C Vemuri, Anand Rangarajan","doi":"10.1109/CVPR.2006.131","DOIUrl":"https://doi.org/10.1109/CVPR.2006.131","url":null,"abstract":"In this paper, we propose a novel and robust algorithm for the groupwise non-rigid registration of multiple unlabeled point-sets with no bias toward any of the given point-sets. To quantify the divergence between multiple probability distributions each estimated from the given point sets, we develop a novel measure based on their cumulative distribution functions that we dub the CDF-JS divergence. The measure parallels the well known Jensen-Shannon divergence (defined for probability density functions) but is more regular than the JS divergence since its definition is based on CDFs as opposed to density functions. As a consequence, CDF-JS is more immune to noise and statistically more robust than the JS.We derive the analytic gradient of the CDF-JS divergence with respect to the non-rigid registration parameters for use in the numerical optimization of the groupwise registration leading a computationally efficient and accurate algorithm. The CDF-JS is symmetric and has no bias toward any of the given point-sets, since there is NO fixed reference data set. Instead, the groupwise registration takes place between the input data sets and an evolving target dubbed the pooled model. This target evolves to a fully registered pooled data set when the CDF-JS defined over this pooled data is minimized. Our algorithm is especially useful for creating atlases of various shapes (represented as point distribution models) as well as for simultaneously registering 3D range data sets without establishing any correspondence. We present experimental results on non-rigid registration of 2D/3D real point set data.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"1 ","pages":"1283-1288"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2006.131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29306319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 63

Robust Tensor Splines for Approximation of Diffusion Tensor MRI Data. 用于逼近扩散张量核磁共振成像数据的稳健张量样条。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2006-06-17 DOI: 10.1109/CVPRW.2006.179

Angelos Barmpoutis, Baba C Vemuri, John R Forder

{"title":"Robust Tensor Splines for Approximation of Diffusion Tensor MRI Data.","authors":"Angelos Barmpoutis, Baba C Vemuri, John R Forder","doi":"10.1109/CVPRW.2006.179","DOIUrl":"10.1109/CVPRW.2006.179","url":null,"abstract":"In this paper, we present a novel and robust spline approximation algorithm given a noisy symmetric positive definite (SPD) tensor field. Such tensor fields commonly arise in the field of Medical Imaging in the form of Diffusion Tensor (DT) MRI data sets. We develop a statistically robust algorithm for constructing a tensor product of B-splines - for approximating and interpolating these data - using the Riemannian metric of the manifold of SPD tensors. Our method involves a two step procedure wherein the first step uses Riemannian distances in order to evaluate a tensor spline by computing a weighted intrinsic average of diffusion tensors and the second step involves minimization of the Riemannian distance between the evaluated spline curve and the given data. These two steps are alternated to achieve the desired tensor spline approximation to the given tensor field. We present comparisons of our algorithm with four existing methods of tensor interpolation applied to DT-MRI data from fixed heart slices of a rabbit, and show significantly improved results in the presence of noise and outliers. We also present validation results for our algorithm using synthetically generated noisy tensor field data with outliers. This interpolation work has many applications e.g., in DT-MRI registration, in DT-MRI Atlas construction etc. This research was in part funded by the NIH ROI NS42075 and the Department of Radiology, University of Florida.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2006 ","pages":"86"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865691/pdf/nihms144130.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28975634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shape-Based Approach to Robust Image Segmentation using Kernel PCA. 基于形状的核PCA鲁棒图像分割方法。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2006-01-01

Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum

引用次数: 0