Abstract
Image segmentation is an important process in computer vision. Most of the currently popular methods tracking the object edge depend on the pixel gray value. Because of the noise in images, the pixel resolving and the precision of the detection method itself, the detected contours are far from smooth and with many tiny zigzags, which will affect the subsequent processing, such as image recognition, image feature detection, etc. This paper puts forwards a novel method to get a smooth contour in the image segmentation process. Firstly, adopt a dynamic programming algorithm to process the object image and to get a global optimal boundary, then smooth and fit the boundary by an adaptive B-spline, which adjusts the control points depending on the contour curvature. Practical numerical experimental results show that this method has stronger edge detection ability and has produced smoother contour curves than other methods and without losing the edge feature at the same time.
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