Application Of Fuzzy Wavelet Algorithm In Feature Extraction And Defect Identification Of Beer

Pattern classification that is basic activity of people in living , working and studying is indispensable . Vision which is a perfect perceptive system of people and picture carrier which can provide more multidimensional information is not substituted by the other information under most circumstance . Accordingly pattern classification and computer vision has profound academic significance , furthermore , amalgamation of both has important application worthiness . Deeply reaserching academic issue of this field has decisive meaning for promoting technique development and improving application worthiness.To all these questions, this paper designs a whole intelligent architecture which has practical value in feature extraction and defect identification . First it uses the knowledge in image processing to process all the images of defect . Then their features become distinct by using the technique of edge detection.In the feature extraction and identification , the paper brings forward the fuzzy and wavelet algorithm which combines the fuzzy theory and wavelet transform technique . The features are extracted by using wavelet transform and then these features are fuzzied . At last the inferencing machine identifies the sort of defect according to the knowledge in the knowledge-base.During the inspecting course contraposing beer quality , it is intractable during disposing and unavoidable in real-life that the interference caused by bottle . So this paper gives the analyzing and exploration making use of combination of depth map and the knowledge of dynamic image.Losts of experiments and stimulations indicate that the algorithm is feasible in theory and the identification and classification is satisfying .