Shape Classification Via Contour Matching Using the Perpendicular Distance Functions

Authors

  • Ratnesh Kumar Dept. Of Computer Science & Engineering, University of Kalyani, kalyani, Nadia, West Bengal-741235, India
  • Kalyani Mali Dept. Of Computer Science & Engineering, University of Kalyani, Kalyani, Nadia, West Bengal-741235, India

Keywords:

Perpendicular distance functions, Principal component analysis, PCA, Straight line, Contour, Moore boundary

Abstract

We developed a novel shape descriptor for object recognition, matching, registration and analysis of two-dimensional (2-D) binary shape silhouettes. In this method, we compute the perpendicular distance from each point on the object contour to the line passing through the fixed point. The fixed point is the centre of gravity of a shape. As a geometrically invariant feature, we measure the perpendicular distance function for each line that satisfies the centre of gravity of an object and one of the points on the shape contour. In the matching stage, we used principal component analysis concerning the moments of the perpendicular distance function. This method gives an excellent discriminative power, which is demonstrated by excellent retrieval performance that has been experimented on several shape benchmarks, including Kimia silhouettes, MPEG7 data set.

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Published

2021-05-25

How to Cite

[1]
R. . Kumar and K. . Mali, “Shape Classification Via Contour Matching Using the Perpendicular Distance Functions”, Int J Eng and Appl Phys, vol. 1, no. 2, pp. 192–198, May 2021.

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