Study Of Coal Rock Recognition Methods Based On Image Processing

The key technology of coal rock recognition based on image was systematically studied in thisthesis. Firstly, the influence factors of coal-rock image acquisition, noise characteristicsclassification and model were introduced. This paper proposes that the wavelet and wavelet packetare used in the coal rock image denoising. The fidelity concept is defined in order to evaluate coalrock image denoising effect. The common image feature extraction method is analysised. Thecharacteristics of coal rock image in wavelet decomposition characteristics and coal rock meshfigure are studied. The approach of the coal rock image multiscale decomposition which combineswith gray level co-occurrence matrix is used for feature extraction. The distance discriminantmethod is studied. The mean texture orientation texture orientation and variance textureorientation texture orientation are proposed. The basic principle of support vector machine isdiseussed emphatically and the method and system of coal-rock image classification andrecognition based on support vector machine are proposed. Collecting device is proposed.Thetheory of BP neural network and wavelet neural network are diseussed emphatically and theminimum mean square error function of BP neural network and structure of wavelet neuralnetwork are improved.On the basis of coal-rock image feature extraction, the classifiers based onMinkowski distanee, support vector machine, BP neural network, improved BP neural networkand wavelet neural network are proposed. The simulation experiments of coal-rock imagerecognition were condueted and the test results are compared and analyzed.