Harris function based active contour external force for image segmentation

Manno-Kovács, Andrea and Szirányi, Tamás (2012) Harris function based active contour external force for image segmentation. PATTERN RECOGNITION LETTERS, 33 (9). pp. 1180-1187. ISSN 0167-8655 10.1016/j.patrec.2012.01.018

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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.

Item Type: ISI Article
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
Depositing User: Andrea Manno-Kovács
Date Deposited: 22 Jun 2017 06:34
Last Modified: 21 Jul 2019 13:58
URI: https://eprints.sztaki.hu/id/eprint/9158

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