2013 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第8页

Shading-Based Shape Refinement of RGB-D Images 基于阴影的RGB-D图像形状细化

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.186

L. Yu, Sai-Kit Yeung, Yu-Wing Tai, Stephen Lin

引用次数: 125

The SVM-Minus Similarity Score for Video Face Recognition 视频人脸识别的SVM-Minus相似度评分

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.452

Lior Wolf, Noga Levy

引用次数: 72

Uncalibrated Photometric Stereo for Unknown Isotropic Reflectances 未知各向同性反射率的未校准光度立体

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.196

Feng Lu, Y. Matsushita, Imari Sato, Takahiro Okabe, Yoichi Sato

引用次数: 63

Towards Fast and Accurate Segmentation 迈向快速、准确的分割

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.250

C. J. Taylor

引用次数: 36

Learning Locally-Adaptive Decision Functions for Person Verification 学习局部自适应决策函数的人员验证

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.463

Z. Li, Shiyu Chang, Feng Liang, Thomas S. Huang, Liangliang Cao, John R. Smith

引用次数: 518

PDM-ENLOR: Learning Ensemble of Local PDM-Based Regressions PDM-ENLOR:基于局部pdm回归的学习集成

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.245

Yen H. Le, U. Kurkure, I. Kakadiaris

{"title":"PDM-ENLOR: Learning Ensemble of Local PDM-Based Regressions","authors":"Yen H. Le, U. Kurkure, I. Kakadiaris","doi":"10.1109/CVPR.2013.245","DOIUrl":"https://doi.org/10.1109/CVPR.2013.245","url":null,"abstract":"Statistical shape models, such as Active Shape Models (ASMs), suffer from their inability to represent a large range of variations of a complex shape and to account for the large errors in detection of model points. We propose a novel method (dubbed PDM-ENLOR) that overcomes these limitations by locating each shape model point individually using an ensemble of local regression models and appearance cues from selected model points. Our method first detects a set of reference points which were selected based on their saliency during training. For each model point, an ensemble of regressors is built. From the locations of the detected reference points, each regressor infers a candidate location for that model point using local geometric constraints, encoded by a point distribution model (PDM). The final location of that point is determined as a weighted linear combination, whose coefficients are learnt from the training data, of candidates proposed from its ensemble's component regressors. We use different subsets of reference points as explanatory variables for the component regressors to provide varying degrees of locality for the models in each ensemble. This helps our ensemble model to capture a larger range of shape variations as compared to a single PDM. We demonstrate the advantages of our method on the challenging problem of segmenting gene expression images of mouse brain.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"35 1","pages":"1878-1885"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86994152","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: 4

Deformable Spatial Pyramid Matching for Fast Dense Correspondences 快速密集对应的可变形空间金字塔匹配

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.299

Jaechul Kim, Ce Liu, Fei Sha, K. Grauman

引用次数: 259

Active Contours with Group Similarity 具有组相似度的活动轮廓

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.382

Xiaowei Zhou, Xiaojie Huang, J. Duncan, Weichuan Yu

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引用次数: 33

Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry 有效的三维终末发射TRUS前列腺分割与全局优化的旋转对称

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.287

Jing Yuan, W. Qiu, Martin Rajchl, E. Ukwatta, X. Tai, A. Fenster

{"title":"Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry","authors":"Jing Yuan, W. Qiu, Martin Rajchl, E. Ukwatta, X. Tai, A. Fenster","doi":"10.1109/CVPR.2013.287","DOIUrl":"https://doi.org/10.1109/CVPR.2013.287","url":null,"abstract":"Segmenting 3D end firing transrectal ultrasound (TRUS) prostate images efficiently and accurately is of utmost importance for the planning and guiding 3D TRUS guided prostate biopsy. Poor image quality and imaging artifacts of 3D TRUS images often introduce a challenging task in computation to directly extract the 3D prostate surface. In this work, we propose a novel global optimization approach to delineate 3D prostate boundaries using its rotational resliced images around a specified axis, which properly enforces the inherent rotational symmetry of prostate shapes to jointly adjust a series of 2D slice wise segmentations in the global 3D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coupled continuous max-flow model, which not only provides a powerful mathematical tool to analyze the proposed optimization problem but also amounts to a new and efficient duality-based algorithm. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-art methods in terms of efficiency, accuracy, reliability and less user-interactions, and reduces the execution time by a factor of 100.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"2211-2218"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90371761","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: 14

BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification BFO满足HOG:基于定向p.d.f梯度直方图的图像分类特征提取

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.102

Takumi Kobayashi

{"title":"BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification","authors":"Takumi Kobayashi","doi":"10.1109/CVPR.2013.102","DOIUrl":"https://doi.org/10.1109/CVPR.2013.102","url":null,"abstract":"Image classification methods have been significantly developed in the last decade. Most methods stem from bag-of-features (BoF) approach and it is recently extended to a vector aggregation model, such as using Fisher kernels. In this paper, we propose a novel feature extraction method for image classification. Following the BoF approach, a plenty of local descriptors are first extracted in an image and the proposed method is built upon the probability density function (p.d.f) formed by those descriptors. Since the p.d.f essentially represents the image, we extract the features from the p.d.f by means of the gradients on the p.d.f. The gradients, especially their orientations, effectively characterize the shape of the p.d.f from the geometrical viewpoint. We construct the features by the histogram of the oriented p.d.f gradients via orientation coding followed by aggregation of the orientation codes. The proposed image features, imposing no specific assumption on the targets, are so general as to be applicable to any kinds of tasks regarding image classifications. In the experiments on object recognition and scene classification using various datasets, the proposed method exhibits superior performances compared to the other existing methods.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"56 1","pages":"747-754"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90411648","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: 93