Abstract
Abstract In this paper, we propose a new model for segmentation of both gray-scale and color images. This model is inspired by the GAC model, the region-scalable fitting model, the weighted bounded variation model and the active contour model based on the Mumford-Shah model. Compared with other active contour models, our new model cannot only make full use of advantages of both edge-based and region-based models, but also maintain more accurate overall message of segmented objects. Moreover, we establish the existence of the global minimum of the new energy functional and analyze the property of it. Finally, numerical results show the effectiveness of our proposed model.
Highlights
The image segmentation problem is fundamental in the field of computer vision, and the aim of it is to divide an image into a finite number of important regions
Inspired by the geometric active contour (GAC) model, energy functional ( . ) and the active contour model based on the Mumford-Shah model [ ], our new model is constructed
According to Figures c, d, we see that our proposed model can segment all the edges accurately. This result is hard to achieve by using the GAC model
Summary
The image segmentation problem is fundamental in the field of computer vision, and the aim of it is to divide an image into a finite number of important regions. In [ , ], the geodesic active contour model (GAC) is defined by the following minimization problem: L(c) min EGAC(c) = For a color image f = (f , f , f ), a new stopping function g(x) is proposed as follows:
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