焦炭显微光学组织的分类识别是判定焦炭质量和指导生产配煤的关键内容，由于传统的空域和频域方法效果都不理想，提出了一种基于小波分解Contourlet变换（WBCT）与局部二进制模式（LBP）融合的焦炭显微图像识别算法。首先，运用WBCT对图像进行多尺度多方向分解，提取出各子频带的统计特征量；然后，在空域采用均匀LBP算子，计算图像LBP特征描述子；最后，根据融合的相似性度量准则判断图像的光学组织类别。与其他方法对比实验结果表明，该算法不仅可获得更高的识别精度，而且具有较强的抗干扰能力，尤其是对泊松分布噪声敏感性较小，因此，适合于焦炭显微图像的分析。

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.