An Improvement on C-V Model

DOI

10.2991/wiet-13.2013.36How to use a DOI?

Keywords

C-V model; Level Set Method; Image Segmen-tation

Abstract

C-V model has the advantage of being able to detect boundaries of objects that are not defined by gradient. However, when detecting these types of edges, the C-V model only considers the average value of each region without local information. As a result, its segmentation result may exist errors, when detecting non-gradient defined boundaries. In order to overcome this problem, we modify the fitting term of classical C-V model with an extra weight. This weight can control the relative height of zero-level contour, so the new method can decrease the segmentation errors. Experiments show that the new model can obtain more accurate results and segment multi-phase images by setting proper weights.

Copyright

© 2013, 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  - Minggang Jing
AU  - Jitao Wu
AU  - Xiaotao Wang
PY  - 2013/12
DA  - 2013/12
TI  - An Improvement on C-V Model
BT  - Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
PB  - Atlantis Press
SP  - 154
EP  - 157
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.36
DO  - 10.2991/wiet-13.2013.36
ID  - Jing2013/12
ER  -