The society of mind
Recognizing objects in a natural environment: a contextual vision system (CVS)
Proceedings of a workshop on Image understanding workshop
Repairing learned knowledge using experience
Artificial intelligence at MIT expanding frontiers
Robotic grasping of unknown objects: a knowledge-based approach
International Journal of Robotics Research
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Function-based generic recognition for multiple object categories
CVGIP: Image Understanding
Functional and physical object characteristics and object recognition in improvisation
Computer Vision and Image Understanding - Special issue of funtion-based vision
Recognition by functional parts
Computer Vision and Image Understanding - Special issue of funtion-based vision
Generic recognition of articulated objects through reasoning about potential function
Computer Vision and Image Understanding - Special issue of funtion-based vision
Interactive recognition and representation of functionality
Computer Vision and Image Understanding - Special issue of funtion-based vision
Computer Vision and Image Understanding - Special issue of funtion-based vision
Computer Vision and Image Understanding - Special issue of funtion-based vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic object recognition using form and function
Generic object recognition using form and function
Efficient search and verification for function based classification from real range images
Computer Vision and Image Understanding
Learning function-based object classification from 3D imagery
Computer Vision and Image Understanding
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This paper presents the framework of the new context-based reasoning components of the GRUFF (Generic Recognition Using Form and Function) system. This is a generic object recognition system which reasons about and generates plans for understanding 3-D scenes of objects. A range image is generated from a stereo image pair and is provided as input to a multi-stage recognition system. A 3-D model of the scene, extracted from the range image, is processed to identify evidence of potential functionality directed by contextual cues. This recognition process considers the shape-suggested functionality by applying concepts of physics and causation to label an object's potential functionality. The methodology for context-based reasoning relies on determining the significance of the accumulated functional evidence derived from the scene. For example, functional evidence for a chair or multiple chairs along with a table, in set configurations, is used to infer the existence of scene concepts such as "office" or "meeting room space." Results of this work are presented for scene understanding derived from both simulated and real sensors positioned in typical office and meeting room environments.