Current challenges in automating visual perception

Abstract

After nearly half a century of computer vision research, application-specific systems are common but the goal of developing a robust, general-purpose computer vision system remains out of reach. Rather than focus on the strengths and weaknesses of current computer vision approaches, this paper will enumerate and investigate the challenges that must be overcome before this goal can be achieved. Key challenges include handling variations in environment or acquisition parameters such as lighting, view angle, distance, and image quality; recognizing naturally occurring as well as intentionally deceptive variations in object appearance; providing robust general-purpose image segmentation and co-registration; generating 3D representations from 2D images; developing useful object representations; providing required knowledge that is not represented in the image itself; and managing computational complexity. Each of these challenges, along with their relevance to solving the vision problem, will be discussed. Understanding these challenges as a whole may provide insight into underlying mechanisms that will provide the backbone of a robust general-purpose computer vision system.