Chen Yufei

Level Set Segmentation Algorithm Based on Image Entropy and Simulated Annealing

The Mumford-Shah model using level set method is more robust than other curve evolution models to detect discontinuities under noisy environment, which has been widely used in the field of image segmentation. However, its usefulness has been limited by several problems in iterative speed and termination. Consequently, a novel segmentation algorithm based on image entropy and simulated annealing is presented. First of all, techniques of curve evolution, level set method and Mumford-Shah functional for segmentation are discussed, followed by the numerical approximation of the model. Secondly, the concept of image entropy is introduced, as well as its application to our algorithm. Thirdly, the principle of simulated annealing and its inspiration to our algorithm is described. Finally, we perform experiments to test the performance of the algorithm using a variety of images and the results show that the proposed algorithm can improve the traditional Mumford-Shah model in iterative speed and termination.

Contour-Based Plant Leaf Image Segmentation Using Visual Saliency

Segmentation based on active contour has been received widespread concerns recently for its good flexible performance. However, most available active contour models lack adaptive initial contour and priori information of target region. In this paper, we presented a new method that is based on active contours combined with saliency map for plant leaf segmentation. Firstly, priori shape information of target objects in input leaf image which is used to describe the initial curve adaptively is extracted with the visual saliency detection method in order to reduce the influence of initial contour position. Furthermore, the proposed active model can segment images adaptively and automatically. Experiments on two applications demonstrate that the proposed model can achieve a better segmentation result.