Natural shape detection based on principal component analysis

Ashok K Samal, Prasana A. Iyengar

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The classical Hough transform, the generalized Hough transforms, and their extensions are quite robust for detection of a large class of objects that can be categorized as industrial parts. These objects are rigid and have fixed shapes, i.e., different instances of the same object are more or less identical. These techniques, and indeed most current techniques, however, do not adequately handle shapes that are more flexible. These shapes are widely found in nature and are characterized by the fact that different instances of the same shape are similar, but not identical, e.g., leaves and flowers. We present a new technique to recognize natural shapes, based on principal component analysis. A set of basis shapes are obtained using principal component analysis. A Hough-like technique is used to detect the basis shapes. The results are then combined to locate the shape in the image. Experimental results show that the approach is robust, accurate, and fast.

Original languageEnglish (US)
Pages (from-to)253-263
Number of pages11
JournalJournal of Electronic Imaging
Volume2
Issue number3
StatePublished - Jul 1 1993

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Hough transforms
principal components analysis
Principal component analysis
leaves

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

Cite this

Natural shape detection based on principal component analysis. / Samal, Ashok K; Iyengar, Prasana A.

In: Journal of Electronic Imaging, Vol. 2, No. 3, 01.07.1993, p. 253-263.

Research output: Contribution to journalArticle

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