Perceptual organization of thin networks with active contour functions applied to medical and aerial images

This paper describes a new method of perceptual organization applied to the extraction of thin networks on aerial and medical images. The key point of our approach is to consider perceptual grouping as a problem of optimization. First the quality of a grouping is defined with a class of functions inspired by the energy functions used for active contours optimization (involving curvature, co-circularity, grey levels, and orientation). Such functions can be computed recursively, and optimized from a local to a global level with an algorithm related to dynamic programming. This is followed by a selection procedure which rates and extracts principal groupings. The validity of our approach is presented with synthetic images, aerial and medical data.