Fast image segmentation by convex minimisation and split Bregman method

• Published in: Electronics letters Vol. 49; no. 17; pp. 1073 - 1074
• Main Authors: Li, W.B, Song, S.H, Luo, F
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 15.08.2013; Institution of Engineering and Technology
• Subjects: Applied sciences; approximation theory; Artificial intelligence; Computer science; control theory; systems; convex energy function; convex minimisation model; Exact sciences and technology; fast image segmentation; global minimiser; Heaviside function; Image and vision processing and display technology; image segmentation; intensity inhomogeneity; local information; minimisation; Pattern recognition. Digital image processing. Computational geometry; split Bregman method; approximation theory; split Bregman method; fast image segmentation; intensity inhomogeneity; image segmentation; global minimiser; convex minimisation model; local information; Heaviside function; minimisation; convex energy function; Image processing; Background; Energy function; Global optimum; Modeling; Convex programming; Image segmentation; Experimental result; Efficiency; Bregman divergence; Convex function; Convex analysis; Pixel
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2013.1114

A convex minimisation model for image segmentation is proposed. The basic idea of this model is that objects will be detected automatically if background is removed. The local information of every pixel is used to make the model applicable to images with intensity inhomogeneity. Also, by using a convex approximation of the Heaviside function, the convex energy function of the proposed model is obtained. Then it is minimised by applying the split Bregman method, which is a fast technique to obtain the global minimiser. The experimental results demonstrate that the proposed model is powerful in efficiency and accuracy.