Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition最新文献_第9页

Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences With an Adaptive Scale Kernel Consensus Estimator. 基于自适应尺度核一致估计的内窥镜图像序列鲁棒运动估计与结构恢复。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-06-23 DOI: 10.1109/CVPR.2008.4587687

Hanzi Wang, Daniel Mirota, Masaru Ishii, Gregory D Hager

引用次数: 49

Least Squares Congealing for Unsupervised Alignment of Images. 图像无监督对齐的最小二乘凝结算法。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-06-23 DOI: 10.1109/CVPR.2008.4587573

Mark Cox, Sridha Sridharan, Simon Lucey, Jeffrey Cohn

引用次数: 94

Enforcing Convexity for Improved Alignment with Constrained Local Models. 增强凸性以改进约束局部模型的对齐。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-06-23 DOI: 10.1109/CVPR.2008.4587808

Yang Wang, Simon Lucey, Jeffrey F Cohn

{"title":"Enforcing Convexity for Improved Alignment with Constrained Local Models.","authors":"Yang Wang, Simon Lucey, Jeffrey F Cohn","doi":"10.1109/CVPR.2008.4587808","DOIUrl":"https://doi.org/10.1109/CVPR.2008.4587808","url":null,"abstract":"Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/tracking in comparison to leading holistic approaches (e.g., AAMs). A major problem hindering the development of CLMs further, for non-rigid object alignment/tracking, is how to jointly optimize the global warp update across all local search responses. Previous methods have either used general purpose optimizers (e.g., simplex methods) or graph based optimization techniques. Unfortunately, problems exist with both these approaches when applied to CLMs. In this paper, we propose a new approach for optimizing the global warp update in an efficient manner by enforcing convexity at each local patch response surface. Furthermore, we show that the classic Lucas-Kanade approach to gradient descent image alignment can be viewed as a special case of our proposed framework. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid face alignment/tracking on the MultiPIE database and the UNBC-McMaster archive.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2008.4587808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29116345","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: 176

Local Minima Free Parameterized Appearance Models. 局部最小自由参数化外观模型。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-06-23 DOI: 10.1109/CVPR.2008.4587524

Minh Hoai Nguyen, Fernando De la Torre

引用次数: 19

Image Segmentation via Convolution of a Level-Set Function with a Rigaut Kernel. 基于Rigaut核的水平集函数卷积的图像分割。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587460

Ozlem N Subakan, Baba C Vemuri

{"title":"Image Segmentation via Convolution of a Level-Set Function with a Rigaut Kernel.","authors":"Ozlem N Subakan, Baba C Vemuri","doi":"10.1109/CVPR.2008.4587460","DOIUrl":"https://doi.org/10.1109/CVPR.2008.4587460","url":null,"abstract":"Image segmentation is a fundamental task in Computer Vision and there are numerous algorithms that have been successfully applied in various domains. There are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present a novel approach that does not make use of any prior information about the objects in the image being segmented. Segmentation is achieved using local orientation information, which is obtained via the application of a steerable Gabor filter bank, in a statistical framework. This information is used to construct a spatially varying kernel called the Rigaut Kernel, which is then convolved with the signed distance function of an evolving contour (placed in the image) to achieve segmentation. We present numerous experimental results on real images, including a quantitative evaluation. Superior performance of our technique is depicted via comparison to the state-of-the-art algorithms in literature.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2008.4587460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27978673","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: 11

Large Margin Pursuit for a Conic Section Classifier. 圆锥截面分类器的大余量追踪。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587406

Santhosh Kodipaka, Arunava Banerjee, Baba C Vemuri

{"title":"Large Margin Pursuit for a Conic Section Classifier.","authors":"Santhosh Kodipaka, Arunava Banerjee, Baba C Vemuri","doi":"10.1109/CVPR.2008.4587406","DOIUrl":"https://doi.org/10.1109/CVPR.2008.4587406","url":null,"abstract":"Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. The discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability.In this paper, we improve the performance of the two-class classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundary generated by the CSC in the input space. We then introduce a large margin pursuit in the learning phase so as to enhance the generalization capacity of the classifier. We validate the algorithm on real datasets and show favorable classification rates in comparison to many existing state-of-the-art binary classifiers as well as the CSC without margin pursuit.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2008.4587406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28017160","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: 6

A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets. 具有同胚水平集的多隔室分割框架。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587475

Xian Fan, Pierre-Louis Bazin, Jerry L Prince

{"title":"A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets.","authors":"Xian Fan, Pierre-Louis Bazin, Jerry L Prince","doi":"10.1109/CVPR.2008.4587475","DOIUrl":"https://doi.org/10.1109/CVPR.2008.4587475","url":null,"abstract":"The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. The computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. The properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2008.4587475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31108040","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: 30

Segmentation of Left Ventricle From 3D Cardiac MR Image Sequences Using A Subject-Specific Dynamical Model. 利用特定受试者的动态模型从三维心脏磁共振图像序列中分割左心室

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587433

Yun Zhu, Xenophon Papademetris, Albert Sinusas, James S Duncan

{"title":"Segmentation of Left Ventricle From 3D Cardiac MR Image Sequences Using A Subject-Specific Dynamical Model.","authors":"Yun Zhu, Xenophon Papademetris, Albert Sinusas, James S Duncan","doi":"10.1109/CVPR.2008.4587433","DOIUrl":"10.1109/CVPR.2008.4587433","url":null,"abstract":"Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of cardiac shapes and deformations. In this paper, we use a subject-specific dynamical model (SSDM) that handles inter-subject variability and temporal dynamics (intra-subject variability) simultaneously. It can progressively identify the specific motion patterns of a new cardiac sequence based on the segmentations observed in the past frames. We formulate the integration of the SSDM into the segmentation process in a recursive Bayesian framework in order to segment each frame based on the intensity information from the current frame and the prediction from the past frames. We perform \"Leave-one-out\" test on 32 sequences to validate our approach. Quantitative analysis of experimental results shows that the segmentation with the SSDM outperforms those with the SM and GDM by having better global and local consistencies with the manual segmentation.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801445/pdf/nihms159128.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28628401","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 L'Âne Rouge: Sliding Wavelets for Indexing and Retrieval. Shape L'Âne Rouge：用于索引和检索的滑动小波

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2008.4587838

Adrian Peter, Anand Rangarajan, Jeffrey Ho

{"title":"Shape L'Âne Rouge: Sliding Wavelets for Indexing and Retrieval.","authors":"Adrian Peter, Anand Rangarajan, Jeffrey Ho","doi":"10.1109/CVPR.2008.4587838","DOIUrl":"10.1109/CVPR.2008.4587838","url":null,"abstract":"Shape representation and retrieval of stored shape models are becoming increasingly more prominent in fields such as medical imaging, molecular biology and remote sensing. We present a novel framework that directly addresses the necessity for a rich and compressible shape representation, while simultaneously providing an accurate method to index stored shapes. The core idea is to represent point-set shapes as the square root of probability densities expanded in a wavelet basis. We then use this representation to develop a natural similarity metric that respects the geometry of these probability distributions, i.e. under the wavelet expansion, densities are points on a unit hypersphere and the distance between densities is given by the separating arc length. The process uses a linear assignment solver for non-rigid alignment between densities prior to matching; this has the connotation of \"sliding\" wavelet coefficients akin to the sliding block puzzle L'Âne Rouge. We illustrate the utility of this framework by matching shapes from the MPEG-7 data set and provide comparisons to other similarity measures, such as Euclidean distance shape distributions.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2008 4587838","pages":"4587838"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921664/pdf/nihms223534.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29194142","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

A Graph Cut Approach to Image Segmentation in Tensor Space. 张量空间图像分割的图割方法。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2008-01-01 DOI: 10.1109/CVPR.2007.383404

James Malcolm, Yogesh Rathi, Allen Tannenbaum

引用次数: 69