Key Techniques On Automatic Recognition Of Coke Optical Texture

Coke is the important fuel and raw materials for blast furnace ironmaking. The microstructureof coke is closely related to its quality, and the microscopic optical texture structure is an importantfactor of coke's degradation in blast furnace, which determines the coke reactivity and post reactionstrength to a great extent. In addition, when coal samples have approximate blending proportion, thecorresponding coke microscopic optical texture structure is also very similar. Moreover, the tinychanges of blending proportion will also be reflected from coke optical texture structure. Therefore,the research on the classification and identification of coke optical texture will help us furtherunderstand the nature of coke and its degradation behavior, and it also has very importantsignificance for evaluation of coke quality and guidance of coking blending.At present, however, the traditional analysis methods on coke microscopic optical texture aremainly rely on artificial numbered statistics according to the standard maps or semi-automaticdetection. Although digital image processing and analyzing technologies have attracted considerableinterest in recent years, the majority of the research results focus on the determination of cokestomatal parameters and the automatic identification research on coke microscopic optical texturedevelop slowly. Particularly, the achievements about successful identification to the small classes ofcoke anisotropic optical texture are rarely few.Based on the above background, through the key technique and difficult problem analysis forautomatic identification of coke optitcal texture, we studied and discussed the following threeaspects in this work: the first one was the spatial resolution enhancement of coke micrographs; andthe second one was the segmentation of different optical texture regions; and the last one was theautomatic classification and identification for different optical textures. The goal is to provide sometheoretical guidance and reference methods, which may provide some useful helps in a certain extentto improve the efficiency, save the energy and reduce the costs in metallurgy coke production.The main research contents and innovative contributions of this dissertation are as follows:(1) Because coke microscopic optical textures have the nature of diversity and complexity, anew two-step learning scheme was proposed in this work. Firstly, two ideal sample libraries wereconstructed with high-and low-resolution coke micrographs. The high resolution estimated imageof any a test sample would be able to preserve standard global features through improved principalcomponent analysis (IPCA) learning algorithm with dynamic training samples and eigenvectors ofcovariance matrices. Then, the concept of "manifold learning" was introduced, and the detailed localinformation of the test sample was obtained by an overlapped patch-based residue prediction usingweighted neighbor linear embedding (WNLE) manifold learning algorithm. Furthermore, theresolution enhancement image was achieved after synthesis. Numerical experimental results demonstrated that the proposed algorithm effectively overcame some common problems intraditional learning methods such as test sample "new data" and image prior estimate of symbioticmodel et al.(2) Based on the principle of convex set optimization, through the feasibility and superiorityanalysis on the convex set projecting under wavelet domain, an improved reconstruction method wasproposed to enhance the coke micrograph resolution for multi-frame video sequence. Two differentconvex sets and projection operators were designed under the wavelet-domain, from the aspects ofinter-frame and intra-frame to extract the details hidden among the adjacent observed low-resolutionframes. Furthermore, a simplified spatial-domain estimator was employed by introducing thepreconditioned conjugate gradient method to forecast the search direction and the step length ofadjacent factors in prediction model. Taking advantage of the spatial estimator to put constraints onthe potential solutions of the POCS, it could get unique optimal or near optimal solution quickly.Experimental results verified the effectiveness and robustness of the proposed algorithm.(3) Since a coke micrograph may consists of two or more different optical texturecompositions, and similar or different component regions have the characteristics with fuzzy anduncertain borders, an improved mean-shift clustering segmentation algorithm was put forward basedon the edge of confidence. Firstly, the edge confidence function was designed according to the imagegradient information. Secondly, based on this function, weight parameters are introduced to thetraditional mean-shift algorithm, which leaded to reduce the iteration times and improve theaccuracy of detected modes. Thirdly, through revising the clustering conditions in both space andcolor domains, the initial clustering results were improved, and different optical texture regions wereeffectively segmented finally.(4) As the results of the traditional methods in spatial and frequency domains are both not ideal,a fusion algorithm was proposed to bridge the gap in this work based on WBCT (Wavelet-BasedContourlet Transform, WBCT) and LBP (Local Binary Pattern, LBP), which realized the completemathematical model description of coke optical texture. Firstly, through the analysis of spectrumaliasing phenomenon about contourlet transformation, a new wavelet-base contourlet transformationwas presented, which would complete the decomposition of coke micrograph for multi-scale andmulti-direction. In particular, four features vectors were extracted out in each sub-band according tothe edge distribution features of the decomposition coefficients. Then, we proposed an improveduniform local binary pattern coding algorithm, and confirmed encoded image histogram as a spatialtexture feature. Finally, according to the designed fusion scheme and similarity measure criteria, theclasses of coke optical textures could be automaticly recognized by the nearest neighbor classifier.Extensive experimental results demonstrated the effectiveness and strong anti-interference performances of our algorithm. The recognition rate was above90%.(5) To solve the diversity and complexity problems of coke optical textures further, andimprove identification accuracy, a novel automatic recognition algorithm was developed based onoptimal contourlet packet transformation, which made full use of the image redundant information.Firstly, coke micrograph was decomposed for multi-scale and multi-direction by a nonsubsampledwavelet transformation (NWT) and a nonsubsampled directional filter banks (NSDFB). In addition,an adaptively weighted algorithm for selecting optimal basis was proposed by introducing the2-directonal2-dimensional PCA method and the virtual sample concept. Finally, the classes of cokeoptical textures were recognized according to the designed similarity measure criteria oneigenvectors of the selected basis. Experimental results are provided to validate the effectiveness,anti-interference and robustness performances of the proposed scheme.