Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition最新文献_第8页

Image Atlas Construction via Intrinsic Averaging on the Manifold of Images. 基于图像流形内禀平均的图像图谱构建。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2010-06-01 DOI: 10.1109/CVPR.2010.5540035

Yuchen Xie, Jeffrey Ho, Baba C Vemuri

{"title":"Image Atlas Construction via Intrinsic Averaging on the Manifold of Images.","authors":"Yuchen Xie, Jeffrey Ho, Baba C Vemuri","doi":"10.1109/CVPR.2010.5540035","DOIUrl":"10.1109/CVPR.2010.5540035","url":null,"abstract":"In this paper, we propose a novel algorithm for computing an atlas from a collection of images. In the literature, atlases have almost always been computed as some types of means such as the straightforward Euclidean means or the more general Karcher means on Riemannian manifolds. In the context of images, the paper's main contribution is a geometric framework for computing image atlases through a two-step process: the localization of mean and the realization of it as an image. In the localization step, a few nearest neighbors of the mean among the input images are determined, and the realization step then proceeds to reconstruct the atlas image using these neighbors. Decoupling the localization step from the realization step provides the flexibility that allows us to formulate a general algorithm for computing image atlas. More specifically, we assume the input images belong to some smooth manifold M modulo image rotations. We use a graph structure to represent the manifold, and for the localization step, we formulate a convex optimization problem in ℝ(k) (k the number of input images) to determine the crucial neighbors that are used in the realization step to form the atlas image. The algorithm is both unbiased and rotation-invariant. We have evaluated the algorithm using synthetic and real images. In particular, experimental results demonstrate that the atlases computed using the proposed algorithm preserve important image features and generally enjoy better image quality in comparison with atlases computed using existing methods.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2010 ","pages":"2933-2939"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103231/pdf/nihms213051.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29901163","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

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2010-06-01 Epub Date: 2010-08-05 DOI: 10.1109/CVPR.2010.5540126

T Syeda-Mahmood, P Turaga, D Beymer, F Wang, A Amir, H Greenspan, K Pohl

引用次数: 0

Diffeomorphic Sulcal Shape Analysis for Cortical Surface Registration. 脑皮层表面配准的微分形沟形分析。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2010-06-01 DOI: 10.1109/CVPR.2010.5540177

Shantanu H Joshi, Ryan P Cabeen, Anand A Joshi, Roger P Woods, Katherine L Narr, Arthur W Toga

引用次数: 0

Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications. 学习归一化切割变体的核：凸松弛及其应用。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2010-01-01 DOI: 10.1109/CVPR.2010.5540076

Lopamudra Mukherjee, Vikas Singh, Jiming Peng, Chris Hinrichs

{"title":"Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications.","authors":"Lopamudra Mukherjee, Vikas Singh, Jiming Peng, Chris Hinrichs","doi":"10.1109/CVPR.2010.5540076","DOIUrl":"10.1109/CVPR.2010.5540076","url":null,"abstract":"We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective - i.e., given a set of training examples with known partitions, how should a basis set of similarity functions be combined to induce NCuts favorable distributions. Such a procedure facilitates design of good affinity matrices. It also helps assess the importance of different feature types for discrimination. Rather than formulating the learning problem in terms of the spectral relaxation, the alternative we pursue here is to work in the original discrete setting (i.e., the relaxation occurs much later). We show that this strategy is useful - while the initial specification seems rather difficult to optimize efficiently, a set of manipulations reveal a related model which permits a nice SDP relaxation. A salient feature of our model is that the eventual problem size is only a function of the number of input kernels and not the training set size. This relaxation also allows strong optimality guarantees, if certain conditions are satisfied. We show that the sub-kernel weights obtained provide a complementary approach for MKL based methods. Our experiments on Caltech101 and ADNI (a brain imaging dataset) show that the quality of solutions is competitive with the state-of-the-art.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"3145-3152"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3063999/pdf/nihms194117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29776716","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

Total Bregman Divergence and its Applications to Shape Retrieval. 布雷格曼总发散及其在形状检索中的应用

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2010-01-01 DOI: 10.1109/CVPR.2010.5539979

Meizhu Liu, Baba C Vemuri, Shun-Ichi Amari, Frank Nielsen

{"title":"Total Bregman Divergence and its Applications to Shape Retrieval.","authors":"Meizhu Liu, Baba C Vemuri, Shun-Ichi Amari, Frank Nielsen","doi":"10.1109/CVPR.2010.5539979","DOIUrl":"10.1109/CVPR.2010.5539979","url":null,"abstract":"Shape database search is ubiquitous in the world of biometric systems, CAD systems etc. Shape data in these domains is experiencing an explosive growth and usually requires search of whole shape databases to retrieve the best matches with accuracy and efficiency for a variety of tasks. In this paper, we present a novel divergence measure between any two given points in [Formula: see text] or two distribution functions. This divergence measures the orthogonal distance between the tangent to the convex function (used in the definition of the divergence) at one of its input arguments and its second argument. This is in contrast to the ordinate distance taken in the usual definition of the Bregman class of divergences [4]. We use this orthogonal distance to redefine the Bregman class of divergences and develop a new theory for estimating the center of a set of vectors as well as probability distribution functions. The new class of divergences are dubbed the total Bregman divergence (TBD). We present the l1-norm based TBD center that is dubbed the t-center which is then used as a cluster center of a class of shapes The t-center is weighted mean and this weight is small for noise and outliers. We present a shape retrieval scheme using TBD and the t-center for representing the classes of shapes from the MPEG-7 database and compare the results with other state-of-the-art methods in literature.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"3463-3468"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3782752/pdf/nihms-220978.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31767173","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 Comparison Using Perturbing Shape Registration. 使用扰动形状配准的形状比较。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5206598

Yifeng Jiang, Erin Edmiston, Fei Wang, Hilary P Blumberg, Lawrence H Staib, Xenophon Papademetris

引用次数: 0

Continuous Maximal Flows and Wulff Shapes: Application to MRFs. 连续最大流和Wulff形状:在mrf中的应用。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-01 DOI: 10.1109/CVPR.2009.5206565

Christopher Zach, Marc Niethammer, Jan-Michael Frahm

引用次数: 43

Optimization of Landmark Selection for Cortical Surface Registration. 皮质表面配准中地标选择的优化。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-01 DOI: 10.1109/CVPR.2009.5206560

Anand Joshi, Dimitrios Pantazis, Hanna Damasio, David Shattuck, Quanzheng Li, Richard Leahy

{"title":"Optimization of Landmark Selection for Cortical Surface Registration.","authors":"Anand Joshi, Dimitrios Pantazis, Hanna Damasio, David Shattuck, Quanzheng Li, Richard Leahy","doi":"10.1109/CVPR.2009.5206560","DOIUrl":"10.1109/CVPR.2009.5206560","url":null,"abstract":"Manually labeled landmark sets are often required as inputs for landmark-based image registration. Identifying an optimal subset of landmarks from a training dataset may be useful in reducing the labor intensive task of manual labeling. In this paper, we present a new problem and a method to solve it: given a set of N landmarks, find the k(< N) best landmarks such that aligning these k landmarks that produce the best overall alignment of all N landmarks. The resulting procedure allows us to select a reduced number of landmarks to be labeled as a part of the registration procedure. We apply this methodology to the problem of registering cerebral cortical surfaces extracted from MRI data. We use manually traced sulcal curves as landmarks in performing inter-subject registration of these surfaces. To minimize the error metric, we analyze the correlation structure of the sulcal errors in the landmark points by modeling them as a multivariate Gaussian process. Selection of the optimal subset of sulcal curves is performed by computing the error variance for the subset of unconstrained landmarks conditioned on the constrained set. We show that the registration error predicted by our method closely matches the actual registration error. The method determines optimal curve subsets of any given size with minimal registration error.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"20-25 ","pages":"699-706"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921659/pdf/nihms224199.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29194140","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

Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil. 专用相控阵线圈获取新生儿脑MR图像的皮质增强组织分割。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2009-01-01 DOI: 10.1109/CVPR.2009.5204348

Feng Shi, Pew-Thian Yap, Yong Fan, Jie-Zhi Cheng, Lawrence L Wald, Guido Gerig, Weili Lin, Dinggang Shen

{"title":"Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil.","authors":"Feng Shi, Pew-Thian Yap, Yong Fan, Jie-Zhi Cheng, Lawrence L Wald, Guido Gerig, Weili Lin, Dinggang Shen","doi":"10.1109/CVPR.2009.5204348","DOIUrl":"10.1109/CVPR.2009.5204348","url":null,"abstract":"The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2009 5204348","pages":"39-45"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2941911/pdf/nihms188810.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29299292","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

Half-Integrality based Algorithms for Cosegmentation of Images. 基于半完整性的图像共分割算法。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2009-01-01

Lopamudra Mukherjee, Vikas Singh, Charles R Dyer

引用次数: 0