20150506-13 贾奎: Robust Object Matching using Low-rank constraint

2015-5-2 19:24| 发布者: 贾伟中科院合肥| 查看: 5857| 评论: 0

摘要: 报告嘉宾2：贾奎（ADSC，新加坡）主持人：董伟生（西安电子科技大学）报告题目：Robust Object Matching using Low-rank constraint and its Applications 报告时间：2015年5月6日晚21:00（北京时间） ... ... ...

【15-13期VALSE Webinar活动】

报告嘉宾2：贾奎（澳门大学）
主持人：董伟生（西安电子科技大学）
报告题目：Robust Object Matching using Low-rank constraint and its Applications http://valser.org/webinar/slide/slides/20150506/Talk_VALSE_Webinar_KuiJia.pdf
报告时间：2015年5月6日晚21:00（北京时间）
文章信息：
[1] Kui Jia, Tsung-Han Chan, Zinan Zeng, Shenghua Gao, Gang Wang, Tianzhu Zhang, and Yi Ma, “ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images”, arXiv:1403.7877, 2014.
[2] Tianzhu Zhang*, Kui Jia*, Changsheng Xu, Yi Ma, and N. Ahuja, “Partial Occlusion Handling for Visual Tracking via Robust Part Matching”, IEEE Conference on Computer Vision and Pattern Recognition, 2014. (* indicates equal contributions)
[3] Zinan Zeng, Shijie Xiao, Kui Jia, Tsung-Han Chan, Shenghua Gao, Dong Xu, and Yi Ma, “Learning by Associating Ambiguously Labeled Images”, IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[4] Zinan Zeng, Tsung-Han Chan, Kui Jia, and Dong Xu, “Finding Correspondence from Multiple Images via Sparse and Low-rank Decomposition”, European Conference on Computer Vision, 2012.
报告简介：Feature-based object matching is a fundamental problem in computer vision. In this talk, we present a new first-order object (inlier features) matching technique called ROML (Robust Object Matching using Low-rank constraint). Given a set of images with extracted inlier and outlier features, ROML aims to simultaneously identify the inlier features from each image, and establish their consistent correspondences across the image set. This is a challenging combinatorial problem. To achieve the goal, ROML leverages the underlying data low-rank property to simultaneously optimize a partial permutation matrix (PPM) for each image, and feature correspondences are established by the obtained PPMs. Extensive experiments on rigid/non-rigid object matching, matching instances of a common object category, and common object localization demonstrate ROML’s efficacy for feature-based object matching. A few more recent examples will also be presented in this talk to show how ROML can be used/adapted for a variety of computer vision applications.
报告人简介: Kui Jia received the B.Eng., M.Eng, and Ph.D. degrees respectively from Northwestern Polytechnic University, National University of Singapore, and Queen Mary, University of London. He is currently a Visiting Faculty with the Faculty of Science and Technology, University of Macau, Macau SAR, China. He is also holding a Research Scientist position at Advanced Digital Sciences Center, Singapore. His research interests are in computer vision, machine learning, and image processing.

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20150506-13 贾奎: Robust Object Matching using Low-rank constraint

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