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20160106-01 徐轶超: Light Field Vision for Transparent Object Categorization

2016-1-1 10:30| 发布者: 彭玺ASTAR| 查看: 6921| 评论: 0

摘要: 【16-01期VALSE Webinar活动】报告嘉宾1:徐轶超(Kyushu University)报告时间:2016年1月6日(星期三)晚20:00(北京时间)报告题目:Light Field Vision for Transparent Object Categorization and Segmentation ...

【16-01期VALSE Webinar活动】

图片 1报告嘉宾1:徐轶超(Kyushu University)
Light Field Vision for Transparent Object Categorization and Segmentation [Slides]
Recognizing the object category and detecting a certain object in the image are two important object recognition tasks, but previous appearance-based methods cannot deal with the transparent objects since the appearance of a transparent object dramatically changes when the background varies. Our proposed methods overcome previous problems using the novel features extracted from a light-field image. We propose a light field distortion (LFD) feature, which is background-invariant, for transparent object recognition. Light field linearity (LF-linearity) is proposed to measure the likelihood of a point comes from the transparent object or not. The occlusion detector is designed to locate the occlusion boundary in the light field image. Transparent object categorization is performed by incorporating the LFD feature into the bag-of-features approach for recognizing the category of transparent object. Transparent object segmentation is realized by solving the pixel labeling problem. An energy function is defined and Graph-cut algorithm is applied for optimizing the pixel labeling problem. The regional term and boundary term are from the LF-linearity and occlusion detector output. Light field datasets (available by request) are acquired for the transparent object categorization and segmentation. The results demonstrate that the proposed methods successfully categorize and segment transparent objects from a light field image.
[1] Yichao Xu, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi, "TransCut: Transparent Object Segmentation from a Light-Field Image", ICCV 2015, Santiago, Chile
[2] Yichao Xu, Kazuki Maeno, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi. "Light Field Distortion Feature for Transparent Object Classification". Computer Vision and Image Understanding (CVIU), Vol.139, pp.122-135, 2015
Yichao Xu is a Postdoc researcher of Laboratory for Image and Media Understanding (LIMU), Kyushu University, Fukuoka, Japan, where he received his Ph.D. degrees in September 2015. His research interests are computer vision and computational photography. He has served as a reviewer for top computer vision conferences CVPR/ICCV/ECCV. Prior to Kyushu University, Yichao received his Master degree from University of Chinese Academy of Sciences, China in 2010, and his Bachelor degree from Beijing Electronic Science and Technology Institute, China in 2007. For more details please visit:


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