Atlantis Press Paper Details: Adaptive snake model with automatic force rectification

Most applications of snake model are domain-specific, while specifying fixed snake coefficients to an image in problem. In this paper, we propose content-triggered adaptive snake model (CASM) to lead all the parameters of snake model to be automatically adapted for various images in the noisy environment. First, the CASM applies a fast estimation method to find the possible ranges of gradient magnitudes of object boundary. As soon as the gradient magnitude of progressing snaxels falls in those ranges, CASM will adapt the weights within the snake forces of these snaxels according to encountered changes in gray levels and influences of various forces in the resided snake segments. And, it simultaneously renormalizes their external and internal forces. After primary convergence, CASM fires a compensation evolution to rectify the unqualified snaxels far from the object boundary. The unqualified snaxels, which are discovered by block-based texture analysis, can be pushed inward or pulled outward to the object border by so-called directional compensation evolution in revived evolutions. The simulation results demonstrate that CASM can improve the performance of snake very much, and outperform Gradient Vector Flow (GVF) in noisy images.

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title:		Adaptive snake model with automatic force rectification
publication:		JCIS-2006 Proceedings
part of series:		Advances in Intelligent Systems Research
ISBN:		978-90-78677-01-7
ISSN:		1951-6851
DOI:		doi:10.2991/jcis.2006.11 (how to use a DOI)
author(s):		Din-Yuen Chan, Cheng-Li Chiu, Wei-Ta Chien
corresponding author:		Din-Yuen Chan
publication date:		October 2006
keywords:		Adaptive snake model, Concavity
abstract:		Most applications of snake model are domain-specific, while specifying fixed snake coefficients to an image in problem. In this paper, we propose content-triggered adaptive snake model (CASM) to lead all the parameters of snake model to be automatically adapted for various images in the noisy environment. First, the CASM applies a fast estimation method to find the possible ranges of gradient magnitudes of object boundary. As soon as the gradient magnitude of progressing snaxels falls in those ranges, CASM will adapt the weights within the snake forces of these snaxels according to encountered changes in gray levels and influences of various forces in the resided snake segments. And, it simultaneously renormalizes their external and internal forces. After primary convergence, CASM fires a compensation evolution to rectify the unqualified snaxels far from the object boundary. The unqualified snaxels, which are discovered by block-based texture analysis, can be pushed inward or pulled outward to the object border by so-called directional compensation evolution in revived evolutions. The simulation results demonstrate that CASM can improve the performance of snake very much, and outperform Gradient Vector Flow (GVF) in noisy images.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		JCIS06-CVPRIP-70.pdf (3887 K)