Harris function based active contour external force for image segmentation

Andrea Kovacs, Tamas Sziranyi

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

Deformable active contour (snake) models are efficient tools for object boundary detection. Existing alterations of the traditional gradient vector flow (GVF) model have reduced sensitivity to noise, parameters and initial location, but high curvatures and noisy, weakly contrasted boundaries cause difficulties for them. This paper introduces two Harris based parametric snake models, Harris based gradient vector flow (HGVF) and Harris based vector field convolution (HVFC), which use the curvature-sensitive Harris matrix to achieve a balanced, twin-functionality (corner and edge) feature map. To avoid initial location sensitivity, starting contour is defined as the convex hull of the most attractive points of the map. In the experimental part we compared our methods to the traditional external energy-inspired state-of-the-art GVF and VFC; the recently published parametric decoupled active contour (DAC) and the non-parametric Chan-Vese (ACWE) techniques. Results show that our methods outperform the classical approaches, when tested on images with high curvature, noisy boundaries.

Original languageEnglish
Pages (from-to)1180-1187
Number of pages8
JournalPattern Recognition Letters
Volume33
Issue number9
DOIs
Publication statusPublished - Jul 1 2012

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Keywords

  • Boundary extraction
  • Gradient vector flow
  • Harris corner detection
  • Vector field convolution

ASJC Scopus subject areas

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

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