In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.
Compared with gray images, colorful images contain more useful information. In this paper, an online active contour model regarding color image matting is proposed. Using our model, the objects of color images are detected according to their colors. For the proposed model, the new scheme firstly identifies the objects to be segmented by setting the initial contour. Then the new energy functional, which is based on the intensities in each channel, is minimized through an efficient level set formula. Thus less iterations and little calculation time are needed. Finally, the morphological opening and closing operation is adopted for regularization. Experiments results demonstrate the efficiency and effectiveness of the proposed approach, compared with the current active contour models.