Image processing for positioning mechanical device with Backpropagation algorithm and separate handling of RGB components

Abstract

Different approaches for the use of Artificial Neural Networks - ANNs, in the recognition of image patterns, have been used with variations ranging from the processing of the image data to the ANN architecture itself. This paper describes the development of a system that aims to recognize patterns of images with ANNs of three inputs that receive images decomposed into their RGB components. The ANNs have an architecture with two hidden layers of six neurons each, and use the algorithm Backpropagation. The built model normalizes RGB components with values ​​between zero and one. The Backpropagation algorithm is used for the purpose of functional approximation of these components, and after training, the numerical arrangements obtained in the three outputs corresponding to the inputs are denormalized to form the resulting training image. Six image pattern had training in different ANNs, forming a system to recognized each pattern. The feasibility of using the model was verified with the tests for its generalization capacity. Images used to position a mechanical device, which did not participate in the training, were inserted into the system and from them the positioning of the device was performed, with a high degree of accuracy.