Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Abstract

With the development of non-destructive testing technology and computer technology, wood non-destructive testing technology will develop towards intelligence and automation. The primary condition for development is to be able to carry out the testing of various physical properties of wood without destroying the wood itself. Wood defects refer to abnormal wood structure. Its existence will affect the quality of wood, change the normal performance of wood, and reduce the utilization rate and use value of wood. The purpose of this article is to try to find an effective detection method without damaging the original structure of the wood. It can accurately and quickly determine the defect information on the wood surface. This research mainly discusses the visual analysis of wood internal defect detection based on tomographic image reconstruction algorithm. According to the analysis of the planks to be tested, this paper determines the structural characteristics, the types of defects to be tested, and the classification standards. To analyze the principle of machine vision imaging, this paper designs a hardware experiment system for wood board imaging, by observing the collected Biyun Temple building wood images and summarizing the Biyun Temple building wood defects, surface texture features, and various appearance features in the image. Based on digital image processing technology, this paper designs a complete set of real-time timber classification and detection algorithms. The algorithm realizes the extraction of Biyun Temple’s architectural wood region, texture region extraction, and the comprehensive feature vector extraction of Biyun Temple’s architectural wood. In the image preprocessing stage, the color image is converted into a grayscale image through the grayscale processing of the image. Through the equalization processing of the histogram, the defect features in the defect image are highlighted. Through image smoothing and denoising processing, the noise points that may exist in the image are removed. The signal-to-noise ratio of the Marching Cube algorithm is maintained at a relatively high level under different noise conditions, which is about 3-4db higher than the Gaussian method with OPED. This research is helpful for the continuation and inheritance of ancient architectural attainments.