Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

Abstract

A narrow band active contour model for color image segmentation is proposed, which applies local statistics to improve the robustness against noise. The crux of our approach is to use intensity mean of local region to define the force function within a level set framework, within which a narrow band is implemented to further improve the computational efficiency. In addition, the image is segmented from channel-to-channel, which shows superior performance when the intensities of the object and background are similar. Furthermore, a multichannel segmentation combination method is used to integrate the information of multiple level sets. The proposed model has been applied to both synthetic and real images with expected results, and the comparison with the state-of-the-art approaches demonstrates the accuracy and superiority of our approach.