Object class recognition in 3D image data

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Developing a solution to the problem of object recognition which automatically identifies and locates items within a scene would achieve one of the fundamental goals of computer vision, placing Canada at the forefront of revolutionary technology. Object recognition is a longstanding and difficult problem, and there has been much progress in recent years, which has led to fast and effective prototypes. At the same time, there is a pull toward the application of recognition methods in commercial systems, initially in fairly constrained domains such as optical character recognition, licence plate readers, gaming, face recognition and biometrics, and increasingly in more general scenarios. The objective of the proposed research is to develop effective object class recognition methods, in general scenes, using 3D range data. Effective object class recognition methods are the essential missing component to the advent of more autonomous intelligent systems. The coming advances to the field will elevate this technology to a high level of utility, enabling a new generation of intelligent robotic systems that can interact more flexibly in human environments. Society will benefit from manufacturing applications, such as flexible automation, but also from widespread deployment into everyday applications such as security and surveillance, and health-care and service robots.