Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

The color of a product is one of the main factors that affect consumer choices. However, it can be difficult to discern the subtle differences in color in order to inspect or classify products without expensive and specialized equipment. In this study, we proposed an approach where standard off-the-shelf components (digital camera and RGB LED lights) can be used to robustly classify products based on their color. By varying the color of the illumination, environment-independent information of the color of the object can be obtained. Then, using a Log-Linearized Gaussian Mixture Neural Network (LLGMN), a neural network based on a statistical model, the objects can be effectively and robustly classified based on their color without requiring a large number of training data. An experiment was performed to verify the effectiveness of this approach. 8 samples with only a subtle difference in color were prepared, and a prototype was developed to be used to classify these 8 samples. We demonstrate that we are able to classify the 8 samples an accuracy of 100% when there was no ambient light and were still able to maintain an accuracy of 94.1% when there was ambient light in the room.