Research For Glass Defective Recognition Technology

Glass is an important and widely used material in producing and living of the contemporary society.Limited by the crafts and environment,its manufacturing process is apt to generate defect.The defect not only undermines the glass's mechanical performance but the thermal stability.So,it does a bad affection for human's daily life and the industrial production.Recently,most glass producers depend on traditional manual method for monitoring and controlling the defective glass.Because of both the subjective and objective factors,the recognition result is bad.In this background,it becomes necessary to design and realize an automatic recognition method for defective glass image.This thesis researches related Pattern Recognition technologies in the glass image field.In conclusion,it has important significance and research value to realize an automatic glass defective recognition system.The main work of this thesis contains as follow:(1) According to the characteristic of the glass images,a lot of experiments have been done to determine the advisable image pre-processing algorithm such as gray processing,space linear transformation and image binaryzation(2) Researched the referred theories on wavelet and wavelet packet,and used the moments invariants(including Hu's moment,Zernike's moments and wavelet moment)to the glass image feature extracting.Besides,designed a new exact wavelet moments algorithm(E-WMI) to solve the computing errors in the present algorithms.(3) Researched the basic theories on SVM and designed a classifier based on it to recognize the defective glass images.According to the disadvantage of present increment training methods,designed a new Symmetrical increment method(S-ISVM) to train the increment samples.(4) Finished the mainly sub-modules with VC++ and MATLAB for the glass defective recognition system and collected these modules to a testing platform.After lots of experiment,it proved that using the methods designed in this thesis has the better performance in the glass defective recognition.The new exact wavelet moments have the better recognition rate compared with other feature extracting methods and the S-ISVM can improve the recognition efficiency.The thesis compares kinds of image pre-processing,feature extracting arithmetic in terms of theory,coding,and results and designs the optimal methods regarding image analysis in glass defect.