Constrained Region-Growing and Edge Enhancement Towards Automated Semantic Video Object Segmentation

  • L. Gao
  • J. Jiang
  • S. Y. Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


Most existing object segmentation algorithms suffer from a so-called under-segmentation problem, where parts of the segmented object are missing and holes often occur inside the object region. This problem becomes even more serious when the object pixels have similar intensity values as that of backgrounds. To resolve the problem, we propose a constrained region-growing and contrast enhancement to recover those missing parts and fill in the holes inside the segmented objects. Our proposed scheme consists of three elements: (i) a simple linear transform for contrast enhancement to enable stronger edge detection; (ii) an 8-connected linking regional filter for noise removal; and (iii) a constrained region-growing for elimination of those internal holes. Our experiments show that the proposed scheme is effective towards revolving the under-segmentation problem, in which a representative existing algorithm with edge-map based segmentation technique is used as our benchmark.


Edge Pixel Object Segmentation Video Object Segmented Object Edge Enhancement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • L. Gao
    • 1
  • J. Jiang
    • 2
  • S. Y. Yang
    • 1
  1. 1.Institute of AcousticsChinese Academy of SciencesChina
  2. 2.School of InformaticsUniversity of BradfordUK

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