Comparison of shape descriptors for mice behavior recognition

  • Authors:

  • Jonathan De Andrade Silva;Wesley Nunes Gonçalves;Bruno Brandoli Machado;Hemerson Pistori;Albert Schiaveto De Souza;Kleber Padovani De Souza

  • Affiliations:

  • Computer Science Department, University of São Paulo at São Carlos, Brazil;Computer Science Department, University of São Paulo at São Carlos, Brazil;Computer Science Department, University of São Paulo at São Carlos, Brazil;Research Group in Engineering and Computing, Dom Bosco Catholic University, Brazil;Department of Morphophysiology, Federal University of Mato Grosso do Sul, Brazil;Research Group in Engineering and Computing, Dom Bosco Catholic University, Brazil

  • Venue:
  • CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
  • Year:
  • 2010

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Abstract

Shape representation provides fundamental features formany applications in computer vision and it is known to be important cues for human vision. This paper presents an experimental study on recognition of mice behavior. We investigate the performance of the four shape recognition methods, namely Chain-Code, Curvature, Fourier descriptors and Zernike moments. These methods are applied to a real database that consists of four mice behaviors. Our experiments show that Zernike moments and Fourier descriptors provide the best results. To evaluate the noise tolerance, we corrupt each contour with different levels of noise. In this scenario, Fourier descriptor shows invariance to high levels of noise.