Target recognition approach using image local features in rehabilitation robots

Abstract

From the computer science literature, it can be seen that many different technologies are used in target recognition, which is one of the most significant areas in the artificial intelligence field. Target recognition is applied in a variety of disciplines, including healthcare, robot vision, vehicular traffic, and virtual reality. Target recognition techniques involve a robotic vision system that must perform with high accuracy and efficiency in real time; additionally, it must have the capacity to handle difficult identification contexts. In one existing target recognition system, the Harris algorithm is used; it provides a higher accuracy compared to more traditional algorithms. In order to improve its achieved accuracy, we focus on the target detection algorithm of a rehabilitation robot that is based on the local features of images. Considering the feature points of the images and target identification technology, a rehabilitation robotic recognition method is developed in this work. Initially, it collects the images, and then, adaptive weighted symplectic geometry decomposition is used for pre-processing. This method helps to reduce the noise in the images. Next, the features are extracted, and the vectors of the features are separated and identified. Afterward, one-to-many rehabilitation modes and actual system monitors are implemented to precisely select the target condition based on the functional criteria of the rehabilitation robot recognition method. Finally, an invertible color-to-grayscale conversion method using clustering and reversible watermarking is applied. It converts images into grayscale. The Gaussian distribution is consistently utilized to define the position and the quantity of the extracted feature points. Related images are retrieved as well. According to experimental findings, the proposed method improves the accuracy and the recall rate compared with the Harris algorithm.