A Fusion Scheme for Coke Micrograph Recognition Based on WBCT and LBP
基于WBCT和LBP融合的焦炭显微图像识别方法

The classification and recognition of the coke optical texture is one of the key elements to determine the quality and guide the production of cokes. As the results of the traditional methods in spatial and frequency domain are not so ideal, a fusion algorithm, which is based on WBCT and LBP, is proposed. Firstly, the method decomposes the coke micrograph with WBCT for multi-scale and multi-direction, and extracts the statistical features from each sub-band. Then in spatial domain, the features of coke micrograph are calculated with uniform LBP operator. Finally, according to the fusion similarity measure criteria, the classes of optical texture in coke micrograph are identified. Comparing with other methods, the results of experiments show that the proposed algorithm not only obtain a high recognition accuracy, but also has strong anti-interference ability, especially for the Poisson distribution of noise. Therefore, it is suitable for micrograph analysis of coke.