A vision system for robotic inspection and manipulation

A model-based approach has been proposed to make object recognition computationally tractable. In this approach, models associated with objects expected to appear in the scene are recorded in the system´s knowledge base. The system extracts various features from the input images using robust, low-level, general-purpose operators. Finally, matching is performed between the image-derived features and the scene domain models to recognize objects. Factors affecting the successful design and implementation of model-based vision systems include the ability to derive suitable object models, the nature of image features extracted by the operators, a computationally effective matching approach, knowledge representation schemes, and effective control mechanisms for guiding the systems´s overall operation. The vision system they describe uses gray-scale images, which can successfully handle complex scenes with multiple object types.<>