Analysis Of Wood Transverse Section Microstructure Based On Computer Vision And Research On Species Identification

For to resolve the difficult question of computer-aided wood identification based on wood transverse section micrograph characteristics, it measured the micrograph characteristics by computer vision technology, and established the wood identification system based on the micrograph characteristics. The main method is described as follows. It determined and analyzed the cellular contour, cellular dimension, tissue proportion, etc. of the wood transverse section on the basis of the second exploitation of the multicolor image manipulation software. And it determined the gray level and texture parameters of wood transverse section micrograph by spatial gray level cooccurrence matrix (GLCM). The texture character were analyzed. Then it researched on the feasibility of wood recognition according to the quantitative characters about micrograph, moreover, it programmed the wood recognition program based on the micrograph content of wood transverse section elementarily.In the experiment, 32 parameters were detemined about the micrograph of wood transverse section. These parameters including fibre(tracheid) cell character(10 items), vessel(resin canal) cell character(8 items), wood ray charcter(2 items), ratio of cell wall and micrograph texture character(11 items). The important factors which affect on micrograph texture parameters are distance(d) and angle( θ) of pair of pixels. According to the analysis of relationship between d, θ and texture parameters respectively, the texture parameters based on d and θ reflect the wood transverse section micrograph texture best.It established the simple model of cell of wood transverse section. And it analyzed the speciality meaning of the texutre parameters. When the proportion of cell wall to cavity is in suitable range, the micrograph texture is delicate, high contrast, strong periodicity, and plenty texture. In this case, if the proportion of cell wall to cavity is larger of smaller, the texture is coarser, less contrast and periodicity, decreasing of texture. According to the species distributing in different range of texture parameters, the most species' texture character is delicate, high contrast, strong periodicity, and plenty texture. At the same time, it analyzed the correlation among the texture pamameters, and the results was that the correlation is more significant.It analyzed the relationship between ratio of cell wall, ratio of cell wall to cavity and texture parameters. The results was that the micrograph texture is more delicate, strong periodicity, high definition, high contrast, more plenty texture content when the ratio of cell wall, ratio of cell wall to cavity are 40~70%, 0.15~0.50 respectively. At the same time, the sample numbers is most when the ratio of cell wall, ratio of cell wall to cavity are in this rage.It analyzed the data by the principal component and induced seven principal components. The results showed that the contribution of first seven principal components to total parameters was above 80 percent. The first seven principal components were texture contrast factor, vessel(resin canal) morphological and dimension factor, texture periodicity and complexityfactor, fiber(tracheid) dimension factor, vessel(resin canal) content and distributing factor, fiber(tracheid) morphological factor, wood ray content factor, respectively. At the same time, it analyzed the correction among the parameters, and extracted 13 parameters which are independent each other and have significance for wood identification from all parameters.It founded the computer aided wood identification algorithm based on image processing according to 13 quantitative parameters. The algorithm was founded on the most similar principle. The key of the algorithm was computing the similar coefficient between the unknown sample and the samples in database. When the similar coefficient is bigger, that is more similar. The methods of computing for similar coefficient were Discriminance of Minimal Difference of Parameters(DMDP), Discriminance of Limen of Tree Compositive Character(DLTC) and Synthetic Weight Similarity of Parameters(SWSP). The contribution of all parameters were considering as same for DMDP and DLTC, but it estimate the similarity between parameters according to the limen for DLTC. DMDP was based on the principal component analysis for wood transverse section micrograph character and computing the similar coefficient according to the contribution of every parameter. The identification results of wood identification program is not the only one. The all records in database are sorted by the similar coefficient. At the same time, the microstructural characteristics, the macrostructural characteristics and the image of three sections are shown in the main interface. The user can testify which is the unknown sample. Then the veracity was improved.According to determined and analyzed of parameters of the wood transverse section micrograph, established of the recognition algorithm and programme of wood recognition method, the results showed that the wood recognition method was feasibility based on the image manipulation. It would offer a new technical means and relevant theory foundation.