Machine Vision System Example - Color Recognition System with MATLAB Implementation

This document presents a practical implementation example of a machine vision system focused on color recognition using MATLAB. The system employs advanced image processing techniques to identify and classify objects based on their color characteristics. Through supervised machine learning algorithms, we train the system to achieve accurate color recognition and perform corresponding operations. The implementation involves key MATLAB functions such as imread() for image acquisition, rgb2hsv() for color space conversion, and regionprops() for object analysis. The system utilizes k-means clustering or support vector machines (SVM) for color classification, with feature extraction based on hue-saturation-value (HSV) components for robust performance under varying lighting conditions. This color recognition system finds applications across multiple domains including autonomous vehicles, industrial automation, quality control, and robotics. By implementing this system, developers can gain practical insights into the role and value of machine vision technologies in real-world applications, understanding critical aspects like color thresholding, morphological operations, and pattern recognition algorithms.