Defects recognition of microlens array using gabor filters and supported vector machine

Defects recognition is an important problem with application to fabrication of MLA(Microlens Array).The focus of this paper is on the problem of feature extraction and classification for defects recognition of MLA.Specifically,we propose using Gabor filters for MLA feature extraction and SVM(Support Vector Machine) for defects detection.a multi-classification method based on support vector machine(SVM) is proposed.According t o statistic learning theory,we use kernel functions to map the training samples into a high dimensional space for training.Combining the testing accuracy of different kernel functions,an optimal kernel function is obtained to solve this problem.By comparing different multi-calssification strategies,a diagnosis model based on DAGS VM(directed acyclic graph SVM) is constructed.Extensive experimentation and comparisons using real data,different features and different classifiers(e.g.,Neural Networks and Support Vector Machine) demonstrate the superiority of the proposed approach which has achieved an average accuracy.