Contour based object detection using part bundles

  • Authors:

  • ChengEn Lu;Nagesh Adluru;Haibin Ling;Guangxi Zhu;Longin Jan Latecki

  • Affiliations:

  • Dept. of Computer and Information Science, Temple University, 324 Wachman Hall, 1805 N Broad St., Philadelphia, PA 19122, USA and Dept. of Electronics and Information Engineering, Huazhong Univers ...;Biotechnology Center, University of Winsconsin-Madison, T129 Waisman Center,1500 Highland Ave, Madison, WI 53705, USA;Dept. of Computer and Information Science, Temple University, 324 Wachman Hall, 1805 N Broad St., Philadelphia, PA 19122, USA;Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and Div Commun and Intelligent Networks, Wuhan National Laboratory for Optoelec ...;Dept. of Computer and Information Science, Temple University, 324 Wachman Hall, 1805 N Broad St., Philadelphia, PA 19122, USA

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2010

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Abstract

In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.