Adaptive snake model with automatic force rectification

DOI

10.2991/jcis.2006.11How to use a DOI?

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

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Din-Yuen Chan
AU  - Cheng-Li Chiu
AU  - Wei-Ta Chien
PY  - 2006/10
DA  - 2006/10
TI  - Adaptive snake model with automatic force rectification
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 45
EP  - 48
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.11
DO  - 10.2991/jcis.2006.11
ID  - Chan2006/10
ER  -