Edge curvature and convexity based ellipse detection method

Dilip K. Prasad, Maylor K.H. Leung, Siu Yeung Cho

Research output: Journal PublicationArticlepeer-review

90 Citations (Scopus)

Abstract

In this paper, we propose a novel ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for grouping with the current edge contour. A two-dimensional Hough transform is performed in an intermediate step, in which we use a new 'relationship score' for ranking the edge contours in a group, instead of the conventional histogram count. The score is found to be more selective and thus more efficient. In addition, we use three novel saliency criteria, that are non-heuristic and consider various aspects for quantifying the goodness of the detected elliptic hypotheses and finally selecting good elliptic hypotheses. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is free from human intervention. The method requires a few seconds in most cases. So, it is suitable for practical applications. The performance of the proposed ellipse detection method has been tested on a dataset containing 1200 synthetic images and the Caltech 256 dataset containing real images. In both cases, the results show that the proposed ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1. Further, the method is robust to the increase in the complexity of the images (such as overlapping ellipses, occluded ellipses), while the performance of the contemporary methods deteriorates significantly.

Original languageEnglish
Pages (from-to)3204-3221
Number of pages18
JournalPattern Recognition
Volume45
Issue number9
DOIs
Publication statusPublished - Sep 2012

Keywords

  • Caltech 256 dataset
  • Ellipse detection
  • Grouping
  • Hough transform
  • Occluded ellipses
  • Overlapping ellipses
  • Real images
  • Saliency

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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