Computer Vision System with Deep Learning for Robotic Arm Control

Abstract

This paper presents a Pattern Recognition System, which can be used in classification applications for hand gestures for control of robotic arms. The system based in three steps, uses feature matching for extracting objects from a scene, edge detector and deep learning. The use of extraction of the region of interest and edges segmentation reduces the amount of processing required to recognize signals, thus speeding up the recognition process. Experimental classification results were positive with good statistical results. The presented data were tested considering four different types of segmentation implementations.