Foveal Wavelet-Based Color Active Contour

A framework for active contour segmentation in vector-valued images is presented. It is known that the standard active contour is a powerful segmentation method, yet it is susceptible to weak edges and image noise. The proposed scheme uses foveal wavelets for an accurate detection of the edges singularities of the image. The foveal wavelets introduced by Mallat (2000) are known by their high capability to precisely characterize the holder regularity of singularities. Therefore, image contours are accurately localized and are well discriminated from noise. Foveal wavelet coefficients are updated using the gradient descent algorithm to guide the snake deformation to the true boundaries of the objects being segmented. Thus, the curve flow corresponding to the proposed active contour holds formal existence, uniqueness, stability and correctness results in spite of the presence of noise where traditional snake approach may fail.