Title :
Research of veneer defect identification based on coupling image decomposition and edge detection
Author :
Chao Wang ; A-chuan Wang
Author_Institution :
Coll. of Inf. & Comput. Eng., Northeast Forestry Univ., Harbin, China
Abstract :
Aiming at the identification difficulty of texture-rich veneer defect images, a new variation model based on coupling image texture-structure decomposition and edge extraction is proposed in this paper. Firstly, An advanced AAFC structure-texture decomposition model is obtained through extending the regular items of AAFC model; Secondly, using the semi-quadratic regularization method to obtain a new model of coupled texture extraction and edge detection, and combining with the Chambolle´s projection algorithm to finish the numerical solving of the now model, and finally realizing the structure-texture decomposition and extracting the edge information of veneer defect images. The experiment results show that the new model can get better edge information while conducting the structure-texture decomposition of veneer defect images, which indicates that the effect of image edge extraction in this paper can be better than separate edge extraction.
Keywords :
edge detection; image texture; production engineering computing; wood processing; wood products; AAFC model; Chambolle´s projection algorithm; advanced AAFC structure-texture decomposition model; coupled texture extraction; coupling image decomposition; coupling image texture-structure decomposition; edge detection; edge information; identification difficulty; image edge extraction; numerical solving; semiquadratic regularization method; texture-rich veneer defect images; variation model; veneer defect identification; Couplings; Data mining; Equations; Image decomposition; Image edge detection; Mathematical model; Numerical models;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463251