Object detection with feature stability over scale space

  • Authors:

  • Cattleya Duanggate;Bunyarit Uyyanonvara;Stanislav S. Makhanov;Sarah Barman;Tom Williamson

  • Affiliations:

  • School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani ...;School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani ...;School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani ...;Faculty of Computing, Information Systems and Mathematics, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey KT1 2EE, UK;Department of Ophthalmology, St. Thomas' Hospital, London SE1 7EH, UK

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2011

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Abstract

This paper proposes a novel segmentation method based on the scale space techniques endowed with a feature stability approach. The novelty of the paper is the lifetime of the space-scale blobs measured not only by their presence and disappearance but by the stability of the features characterizing the objects of interest as well. Our numerical experiments show that the algorithm outperforms the conventional space scale algorithm applied to variable size and variable shape objects. The proposed algorithm can be used as a preprocessing step in object or pattern recognition applications to produce seeds for more accurate image segmentation methods such as the snakes or the level set techniques.