A geometric image segmentation method based on a bi-convex, fuzzy, variational principle with teaching-learning optimization

This paper proposes an efficient, bi-convex, fuzzy, variational (BFV) method with teaching and learning based optimization (TLBO) for geometric image segmentation. Firstly, we adopt a bi-convex, object function to process a geometric image. Then, we introduce TLBO to maximally optimize the length-penalty item, which will be changed under the teaching phase and the learner phase of the TLBO. This makes the length penalty item closer to the target boundary. Therefore, the length-penalty item can be automatically adjusted according to the fitness function, namely the evaluation standards of the image quality. At last, we combine the length-penalty item with the numerical remedy mechanism to achieve better results. Compared with existing methods, simulations show that our method is more effective.