A Wood Texture Analysis Method Based On GrayLevel Run And Guass-Markov Randomfield

In recent years,according to the development of image processing and pattern recognition theory,the research of texture analysis and recognition theory has made a series of breakthrough. Texture analysis for image feature extraction,Image analysis and identification,computer vision and so on has important significance.But due to the complexity of image texture analysis, it has become one of the most attractive and difficult problems to digital image processing and pattern recognition.In this paper, we adopt five kinds of wood texture image as the research:white birch,pine,larch,northeast China ash, oak. According to the gray, image denoising, the image of sharpening etc image preprocessing technology, we can obtain facilitate computer processing images.In mathematics markov random field is a vevy good probability model to express the agglomeration. Its statistical parameter can be shown neighborhood set size and direction, and reasonable to describe the image texture random charateristics.But gray longer journeys can be a very good description of the Yankees based on texture of length and grayscale continuous probability combining bothcomprehensive features characteristic parameters parameter system formed,we can Classify and recognize.Because the limitations about the traditional bayesian classifier,it is difficult for usto optimization of wood texture of recognition and classification. Therefore in this paper we chooses this method of synthetical characteristics of wood tecture fourteen characteristic parameters for processing, and in the smallest decisions of errors with ATR neural network classifier use. Experimental results show that application of the proposed classification method for for image classification and recognition We can obtain higher classification speed and a good classification effect.