Automatic classification of optical defects of mirrors from ronchigram images using bag of visual words and support vector machines

Abstract

The Ronchi test is known as a procedure that is able to generate visual patterns called ronchigrams. These patterns could be used to determine optical characteristics on the surface of mirrors, particularly to quantify and qualify optical aberrations and deformations. This paper presents an automatic method to detect these optical errors of mirrors using the Ronchi test by classifying ronchigram images using bag of visual words (BoVWs) for image representation and support vector machines (SVM) for ronchigrams classification. The ronchigram image data set was obtained from the optical manufacture laboratory of lenses and mirrors at Universidad de los Llanos. The BoVWs approach used was based on Scale-Invariant Feature Transform (SIFT) as visual words and a Linear SVM was trained for automatic classification of ronchigrams into optical defects. The classification performance achieved was 0.69% in terms of accuracy measure. These results shows that our proposed approach can be used to detect optical defects of mirrors with high precision in a real scenario of ronchigrams obtained from mirrors during the manufacture process of a optical laboratory.