Yüzey doku örüntüsü sınıflandırma amaçlı görüntü işleme

Conventional pattern classification systems have mostly employed binary classification methods where training features are extracted from multiple classes. This approach faces a challenge when it comes to systems which deal with imbalanced distribution of class samples, a typical characteristic of defect detection systems where there also exists a wide spectrum of possible defects. Using binary classifiers in such a scenario is bound to introduce uncertainties with respect to classifier performance as defects which had not been used in the training stage are encountered. One class classifiers have been proposed to overcome this challenge by using only normal samples to train the classifier. This thesis provides a comprehensive analysis of one class (i.e. unitary) classification to provide an empirical evaluation of the effects of defect spectrum in the feature space. Different unitary classifiers were compared to common binary classifiers and experimental results showed significant instability in the performance of the binary classifiers when classes occupy different regions in the feature space relative to the training classes. The performance of unitary classifiers was stable in all defect type scenarios.