We present a snake-based scheme for efficiently detecting contours of objects with complex boundary concavities. The proposed method is composed of two steps. First, the object's boundary is detected using the proposed snake model. Second, snake points are optimized by inserting new points and deleting unnecessary points to better describe the object's boundary. We use the Frenet formula to calculate the binormal vector at snake points and use the result to control the direction of movement for snake points near boundary concavities. The proposed algorithm can successfully extract objects with boundary concavities. Experimental results have shown that our algorithm produces more accurate segmentation results than the conventional algorithm.
A snake-based algorithm for segmenting an object from a pair of stereo images is presented. Unlike previously developed snake-based algorithms, this one performs well even when the objects in the picture are occluded and the background behind them is cluttered. Also, the algorithm is not sensitive to the placement of the initial snake points. The algorithm uses a new energy function defined over the disparity space between the pair of the stereo images to successfully locate the boundary of an object in a complex image. Experimental results have shown that the developed algorithm produces more accurate segmentation results than those of the well-known conventional snake algorithm reported by Kass et al.
KEYWORDS: Motion estimation, Image filtering, 3D image processing, Filtering (signal processing), Optical filters, Signal to noise ratio, Electronic filtering, Image analysis, 3D modeling, Linear filtering