2013 IEEE International Conference on Computer Vision最新文献_第3页

Markov Network-Based Unified Classifier for Face Identification 基于马尔可夫网络的人脸识别统一分类器

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.245

Wonjun Hwang, Kyungshik Noh, Junmo Kim

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引用次数: 4

Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation 无监督人脸检测器自适应的概率弹性部分模型

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.103

Haoxiang Li, G. Hua, Zhe L. Lin, Jonathan Brandt, Jianchao Yang

引用次数: 85

Modeling Occlusion by Discriminative AND-OR Structures 基于鉴别与或结构的遮挡建模

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.318

Bo Li, Wenze Hu, Tianfu Wu, Song-Chun Zhu

{"title":"Modeling Occlusion by Discriminative AND-OR Structures","authors":"Bo Li, Wenze Hu, Tianfu Wu, Song-Chun Zhu","doi":"10.1109/ICCV.2013.318","DOIUrl":"https://doi.org/10.1109/ICCV.2013.318","url":null,"abstract":"Occlusion presents a challenge for detecting objects in real world applications. To address this issue, this paper models object occlusion with an AND-OR structure which (i) represents occlusion at semantic part level, and (ii) captures the regularities of different occlusion configurations (i.e., the different combinations of object part visibilities). This paper focuses on car detection on street. Since annotating part occlusion on real images is time-consuming and error-prone, we propose to learn the the AND-OR structure automatically using synthetic images of CAD models placed at different relative positions. The model parameters are learned from real images under the latent structural SVM (LSSVM) framework. In inference, an efficient dynamic programming (DP) algorithm is utilized. In experiments, we test our method on both car detection and car view estimation. Experimental results show that (i) Our CAD simulation strategy is capable of generating occlusion patterns for real scenarios, (ii) The proposed AND-OR structure model is effective for modeling occlusions, which outperforms the deformable part-based model (DPM) DPM, voc5 in car detection on both our self-collected street parking dataset and the Pascal VOC 2007 car dataset pascal-voc-2007}, (iii) The learned model is on-par with the state-of-the-art methods on car view estimation tested on two public datasets.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"17 1","pages":"2560-2567"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81751385","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: 35

Complementary Projection Hashing 互补投影哈希

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.39

Zhongming Jin, Yao Hu, Yuetan Lin, Debing Zhang, Shiding Lin, Deng Cai, Xuelong Li

引用次数: 58

Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model 基于优化零件混合和级联可变形形状模型的无姿态面部地标拟合

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.244

Xiang Yu, Junzhou Huang, Shaoting Zhang, Wang Yan, Dimitris N. Metaxas

引用次数: 251

Semi-supervised Learning for Large Scale Image Cosegmentation 大规模图像共分割的半监督学习

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.56

Zhengxiang Wang, Rujie Liu

{"title":"Semi-supervised Learning for Large Scale Image Cosegmentation","authors":"Zhengxiang Wang, Rujie Liu","doi":"10.1109/ICCV.2013.56","DOIUrl":"https://doi.org/10.1109/ICCV.2013.56","url":null,"abstract":"This paper introduces to use semi-supervised learning for large scale image co segmentation. Different from traditional unsupervised cosegmentation that does not use any segmentation ground truth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. This would be a much practical way to effectively co segment a large number of related images simultaneously, where previous unsupervised co segmentation work poorly due to the large variances in appearance between different images and the lack of segmentation ground truth for guidance in co segmentation. For semi-supervised co segmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intra-image distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each sub-problem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed co segmentation method can effectively co segment hundreds of images in less than one minute. And our semi-supervised co segmentation is able to outperform both unsupervised co segmentation as well as fully supervised single image segmentation, especially when the training data is limited.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"2020 1","pages":"393-400"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87830091","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: 26

Exemplar-Based Graph Matching for Robust Facial Landmark Localization 基于样例的图像匹配鲁棒人脸地标定位

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.131

Feng Zhou, Jonathan Brandt, Zhe L. Lin

引用次数: 112

A Simple Model for Intrinsic Image Decomposition with Depth Cues 一种基于深度线索的简单图像分解模型

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.37

Qifeng Chen, V. Koltun

引用次数: 190

Semantic Segmentation without Annotating Segments 没有注释段的语义分割

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.271

W. Xia, Csaba Domokos, Jian Dong, L. Cheong, Shuicheng Yan

引用次数: 43

Category-Independent Object-Level Saliency Detection 类别无关的对象级显著性检测

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.221

Yangqing Jia, Mei Han

引用次数: 133