Method for cooperatively classifying perceived solid wood panel surface textures and defects by feature extraction and compressive sensing based on dual-tree complex wavlet

Abstract

The invention discloses a method for cooperatively classifying perceived solid wood panel surface textures and defects by feature extraction and compressive sensing based on dual-tree complex wavlet, and relates to the field of solid wood panel surface defect detecting. The method is used for solving the problems of low classifying precision, low classifying efficiency, and the like of the existing solid wood panel surface texture and defect classifying method. The method comprises the following steps: performing feature dimension reduction after performing feature extraction by dual-tree complex wavelet transform on solid wood panel images; classifying optimized feature vectors based on a compressive sensing theory; using the optimized feature vectors as a sample row, and establishing a data dictionary matrix by a training sample matrix; linearly representing a measuring sample by using training samples, calculating a sparse representation vector on a data dictionary of a test sample, and determining the category with smallest residual error as the category of the test sample. Due to good directionality of the dual-tree complex wavlet, complex information of the panel surface can be expressed, and the classifying efficiency can be further improved based on feature selection of a particle swarm algorithm. Compared with the conventional classifier, the compressive sensing classifier is simple in structure and relatively high in classifying precision.

Description

technical field [0001] The invention relates to a method for collaborative classification of surface texture and defects of solid wood boards, and relates to the technical field of detection of surface textures and defects of solid wood boards. Background technique [0002] The detection and optimization of the surface of solid wood panels is an important process in the production process, which will directly affect product quality and production efficiency. Before the processing and application of solid wood panels, the defects and texture of solid wood panels should be detected first. Domestic research on surface defect detection, color, and texture of boards mainly includes the research on the surface roughness detection of Northeast Forestry University, the wood surface texture pattern recognition method based on computer vision, the quantitative research on wood texture based on digital image processing, And Nanjing Forestry University's color moment-based wood defect ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for collaborative classification of solid wood board surface texture and defects based on dual-tree complex wavelet feature extraction and compressed sensing to solve the problems of low classification accuracy and classification efficiency in the existing solid wood board surface texture and defect classification methods low level problem

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