Robotic Image Segmentation Architecture based on Deep Reinforcement Learning with Image Differencing

Abstract

The purpose of this study is to propose a method for Image Segmentation without hand-made training data, by moving a camera itself and generating the differential image for the object. The recent image segmentation methods which added the way of a deep learning has greatly been improved in its speed and accuracy aspects, and increasingly been used in the various fields. The output of these methods has been done in the way of end-to-end without person’s help, by providing with a large amount of training data necessary to their learning. However, the training data for the good result and accuracy have to need the time and effort of many persons, and right answers in them have to be certainly included. Otherwise, the correct results are generally not expected. Thus, as a way to solve these problems required to make a large amount of hand-made training data, our proposed method will be useful. Here, the effectiveness of the proposed method will be demonstrated through the experimental results obtained by using image segmentation architecture composed to include a robot.