Editorial: Machine Vision for Assistive Technologies

Abstract

The last decade has witnessed the significant impact of Computer Vision and Robotics on real-world 12 products. The traditional Computer Vision problems such as tracking, 3D reconstruction, detection, 13 recognition, odometry, navigation, and ultimately, are now solved with significantly higher accuracy 14 using Machine Learning (Farinella et Al., 2020). However, most of these results have focused on 15 constrained application scenarios that do not involve the integration of feedback from the user (Leo et 16 Al., 2019). Since these applications do not consider the user's intentions and goals, they tend to be of 17 limited use when it is necessary to assist humans. 18With the pervasive successes of Computer Vision and Robotics and the advent of industry 4.0, it has 19 become paramount to design systems that can truly assist humans and augment their abilities to tackle 20 both physical and intellectual tasks. We broadly refer to such systems as "assistive technologies" (Leo 21 et Al, 2017). Examples of these technologies include approaches to assist visually impaired people to 22 navigate and perceive the world, wearable devices which make use of artificial intelligence, mixed and 23 augmented reality to improve perception and bring computation directly to the user, and systems 24 designed to aid industrial processes and improve the safety of workers (Leo and Farinella, 2018