k-Nearest Neighbor Classification for Pattern Recognition of a Reference Source Light for Machine Vision System

Abstract

The design of machine vision applications allows automatic inspection, measuring systems, and robot guidance. Typical applications of industrial robots are based on no-contact sensors to give the robot information about the environment. Robot’s machine vision requires photosensors or video cameras to make intelligent decisions about its localization. Video cameras used as image-capturing equipment are too costly in comparison with optical scanning systems (OSS). The OSS system provides spatial coordinates measurements that can be exploited to solve a wide variety of structural problems in real-time. Localization and guidance using machine learning (ML) techniques offer advantages due to signals captured can be transformed and be reduced for processing, storage, and displaying. The use of algorithms of ML enhances the performance of the optical system based on localization and guidance. Feature extraction represents an important part of ML techniques to transform the original raw data onto a low-dimensional subspace and holding relevant information. This work presents an improvement of an optical system based on <inline-formula> <tex-math notation= LaTeX >$\\textit k$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation= LaTeX >$\\textit k$ </tex-math></inline-formula>-NN) technique to solve the object detection and localization problem. The utility of this improvement allows the optical system can discriminate between the reference source and the optical noise or interference. The OSS system presented in this article has been implemented in structural health monitoring to measure the angular position even under “lighting and weather conditions”. The feature extraction techniques used in this article were linear predictive coding (LPC), quartiles (<inline-formula> <tex-math notation= LaTeX >$\\textit Q_{iquartile}$ </tex-math></inline-formula>), and autocorrelation coefficients (ACC). The results of using <inline-formula> <tex-math notation= LaTeX >$\\textit k$ </tex-math></inline-formula>-NN and autocorrelation coefficients and quartiles predicted more than 98% of correct classification by using a reference source light as a class 1 and a light bulb as an optical noise and called class 2.