Open AccessImage Segmentation Using GAC Model Combining with GVF and Balloon Force
Author(s) -
Haiping Xu,
Zhenmei Lin,
Yanqing Guo,
Meiqing Wang
Publication year - 2015
Publication title -
journal of algorithms and computational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.234
H-Index - 13
eISSN - 1748-3026
pISSN - 1748-3018
DOI - 10.1260/1748-3018.9.2.177
Subject(s) - vector flow , geodesic , active contour model , force field (fiction) , convergence (economics) , segmentation , artificial intelligence , object (grammar) , enhanced data rates for gsm evolution , computer vision , computer science , vector field , field (mathematics) , image segmentation , mathematics , geometry , pure mathematics , economics , economic growth
The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multi-objects images correctly with higher convergence.
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