Multiblob Cosmetic Defect Description/Classification Using a Fuzzy Hierarchical Classifier

Abstract

The complexity of classification tasks in computer vision applications has increased, as these systems have been adopted in more real-world applications. One of the hardest tasks to model by a computer system is related to achieving a human task based on subjective perception of the environment or goods. This paper presents a fuzzy classifier aimed to perform a subjective classification task, using the same criteria considered by a human inspector. The task consists on the description and classification of a cosmetic defect presented in ophthalmic lenses. The human inspector is modeled by a fuzzy hierarchical rule classifier (FHRC). The goal of the first stage is to obtain the description of the defect constituents. This information is then analyzed by the second stage to provide the final classification. The performance of the FHRC is 91.83% of correct classification in the single blob case and 81.48% in the multiblob. This performance can be considered acceptable, assuming that a human inspector has an estimated performance of 85%, with the advantages that the proposed system does not suffer from the disadvantages of a human inspector. The system also provides information of the defect constituents, which can be used to improve the fabrication process.