06107974

a

06107974 Bai, Xiang Li, Quannan Ma, Tianyang Liu, Wenyu Latecki, Longin Jan Binarization of gray-level images based on skeleton region growing. Brimkov, Valentin E. (ed.) et al., Digital geometry algorithms. Theoretical foundations and applications to computational imaging. New York, NY: Springer (ISBN 978-94-007-4173-7/hbk; 978-94-007-4174-4/ebook). Lecture Notes in Computational Vision and Biomechanics 2, 145-164 (2012). 2012 New York, NY: Springer EN gray scale image binarization skeleton strength map

doi:10.1007/978-94-007-4174-4_5

Summary: In this chapter, we introduce a new binarization method of gray level images. We first extract skeleton curves from Canny edge image. Second, a skeleton strength map (SSM) is calculated from Euclidean distance transform. Starting from the boundary edges, the distance transform is firstly computed and its gradient vector field is calculated. After that, the isotropic diffusion is performed on the gradient vector field and the SSM is computed from the diffused vector field. It has two advantages that make it useful for skeletonization: 1) the SSM serves as the form of the likelihood of a pixel being a skeleton point: the value at pixels of the skeleton is large while at pixels that are away from the center of object, the SSM value decays very fast; 2) By computing the SSM from the distance transform, a parameterized noise smoothing is obtained. Then, skeleton curves are classified into foreground and background classes by comparing the mean value of their local edge pixels and neighbors lowest intensity. Finally, the binarization result is obtained by choosing foreground skeleton curve pixels as seed points and presenting region growing algorithm on gray scale image with certain growing criteria. Images with different types of document components and degradations are used to test the effectiveness of the proposed algorithm. Results demonstrate that the method performs well on images with low contrast, noise and non-uniform illumination.