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Multi-instance object segmentation with occlusion handling
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012
doi:10.1109/cvpr.2015.7298969
dblp:conf/cvpr/ChenLY15
fatcat:lqdek3l74rg2rbfuvd6frucxcq