POLIBITS, Vol. 57, pp. 75-80, 2018.
Abstract: classification algorithms, computer vision, manufacturing automation, pattern recognition
Keywords: In this paper, an algorithm for classification of screw nuts by means of digital image processing is presented. This work is part of a project where a production line was built, and is focused on the quality assessment section. The algorithm presented classifies among good and poor quality screw nuts passing by a conveyor belt, by computing Hu’s moment invariants of its picture. Those moment invariants are the input of a minimum distance classifier, obtaining very competitive results compared with some other classification algorithms of the WEKA platform.
PDF: Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance
PDF: Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance
https://doi.org/10.17562/PB-57-8
Table of contents of POLIBITS 57