The Research Of Wood Surface Color Classification Based On Computer Vision

Wood surface color is an important feather to reflect the visual and psychological feelings of wood surface, and it is directly related to the quality evaluation of wood products and environmental indoors, so the research of presentation and analysis on wood surface color is valuable. In this paper five tree species which are common in northeast are researched, such as Pinus koraiensis, Larix gmelinii, and Quercus Mongolic. The quantitative analysis of wood surface color is carried on by using computer vision technology and the wood is automatic classified according to the feathers of wood surface color.The common color models and their features in the process of color images are introduced in this article, and the preprocess of acquired wood images is finished, such as noise removal, color enhancement and so on. The wood surface color characters of wood images after preprocessing are abstracted with the methods of color histogram and color matrix. Moreover the 3-rank moment parameters, as the wood surface color feathers, are abstracted from color characteristic matrix based on the R, G and B color characteristic integration. Through the analyses of each kind of feature parameters, the classification is forecasted from the distribution of a distance category and mean square.The characteristics of pattern-recognition and neural network classification are introduced in this article. Because the BP neural network has powerful nonlinear shine capacity, the BP neural network classification device is designed. Classification experiments shows that the identification rate of color histograms classification is lower;especially feature parameters for color classification of tangential samples are invalid. The main reason is the loss of color information in the course of color space quantization. The identification rate of classification with color moment feathers is higher for radial and tangential samples. When classified basing on the integrated features, the correct rate of classification of radial samples is the same as that of color moment feathers, and the vector-dimension is lower, so the processing speed is accelerated. For this reason, the integrated features can describe the wood surface color characters of radial wood very well, but they are invalid for tangential samples. Finally comparing the all round performances of three sets parameters, the RGB color moment parameters are the best parameters for wood surface color classification.The results of this research can achieve automatic classification of wood surface color, and improve the automatic level of classification of wood surface color in production processing and wood utilizations. It provides advanced research tools for wood and enriches classification methods of color analysis on the field of computer vision.