Xinggang Wang   Associate professor

王兴刚,华中科技大学,电信息学院,华中卓越学者晨星岗副教授。主要研究方向为计算机视觉和深度学习,研究工作见:https://xinggangw.info。分别于2009年和2014年在华中科技大学获得学士和博士学位,博士期间在美国天普大学、加州大学洛杉矶分校(UCLA)访问研究。在IEEE TPAMI、CVPR、ICML等顶级期刊会议发表学术论文50余篇。谷歌学术引用次数超过7000次。担任CVPR 2022领域主席,Pattern Recognition (IF 7.196)、Image and ...Detials

Fan Shape Model for Object Detection

Release time:2021-06-10  Hits:

  • Indexed by:会议论文
  • First Author:Wang,Xinggang,Wang,Xinggang
  • Correspondence Author:Wang,Xinggang
  • Co-author:Latecki,Jan,Longin,Liu,Wenyu,Ma,Tianyang,Bai,Xiang
  • Journal:2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • Date of Publication:2012-06-16
  • Abstract:We propose a novel shape model for object detection called Fan Shape Model (FSM). We model contour sample points as rays of final length emanating for a reference point. As in folding fan, its slats, which we call rays, are very flexible. This flexibility allows FSM to tolerate large shape variance. However, the order and the adjacency relation of the slats stay invariant during fan deformation, since the slats are connected with a thin fabric. In analogy, we enforce the order and adjacency relation of the rays to stay invariant during the deformation. Therefore, FSM preserves discriminative power while allowing for a substantial shape deformation. FSM allows also for precise scale estimation during object detection. Thus, there is not need to scale the shape model or image in order to perform object detection. Another advantage of FSM is the fact that it can be applied directly to edge images, since it does not require any linking of edge pixels to edge fragments (contours).