1.华南理工大学 电子与信息学院 广州 510640 2.School of Informatics, University of Bradford, BRADFORD BD7 1DP UK
An AntiGeometric Diffusion Classification Based Image Binarization Method
HUANG Qian1, WU Yuan2, YIN JunXun1
1.School of Electronic and Information Engineering, South China University of Technology, Guangzhou 5106402. School of Informatics, University of Bradford, BRADFORD BD7 1DP UK
Abstract：It is difficult to extract objects from background due to the uneven and complex background information. In this paper, a binarization method is developed, which is based on the antigeometric diffusion, a special form of the anisotropic diffusion. The antigeometric diffusion method is used to blur and diffuse the edge of images as much as possible, and thus many threshold surfaces are formed. Each pixel is classified during the diffusion process according to the developed classification criterions. Finally, a postprocessing approach is proposed to extract the object from background. The numerical experimental results show that the presented method is robust to the noise restriction. Furthermore, the results for handling Xray images of casting products with uneven background by the presented method are given.
[1] Chan F H Y, Lam F K, Zhu H. Adaptive Thresholding by Variational Method. IEEE Trans on Image Processing, 1998, 17(3): 468473 [2] Yanowitz S D, Bruckstein A M. A New Method for Image Segmentation. Computer Vision on Graphics and Image Processing, 1989, 46(1): 8295 [3] Perona P, Malik J. Scale Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629639 [4] Manay S, Yezzi A. AntiGeometric Diffusion for Adaptive Thresholding and Fast Segmentation. IEEE Trans on Image Processing, 2003, 12(11): 13101323 [5] Cottet G H, Ayyadi M E. A Volterra Type Model for Image Processing. IEEE Trans on Image Processing, 1998, 7(3): 292303 [6] Catté F, Lions P L, Morel J M, et al. Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. SIAM Journal on Numerical Analysis, 1992, 29(1): 182193 [7] Mery D, Filbert D. Automated Flaw Detection in Aluminum Castings Based on the Tracking of Potential Defects in a Radioscopic Image Sequence. IEEE Trans on Robotics & Automation, 2002, 18(6): 890901 [8] Parker J R. Gray Level Thresholding in Badly Illuminated Images. IEEE Trans on Pattern Analysis and Machine Intelligence, 1991, 13(8): 813819 [9] Trier O D, Jain A K. GoalDirected Evaluation of Binarization Methods. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(12): 11911201 [10] Trier O D, Taxt T. Evaluation of Binarization Methods for Document Images. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(3): 312315 [11] Carmona R A, Zhong S. Adaptive Smoothing Respecting Feature Directions. IEEE Trans on Image Processing, 1998, 7(3): 353358 [12] You Y L, Xu W, Tannenbaum A, et al. Behavioral Analysis of Anisotropic Diffusion in Image Processing. IEEE Trans on Image Processing, 1996, 5(11): 15391553 [13] Witkin A P. ScaleSpace Filtering // Proc of the International Joint Conference on Artificial Intelligence. Karlsruhe, Germany, 1983: 10191021
2008, Vol. 21 
