Image Segmentation using Domain Constraint Knowledge
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{'SignBlockName': 'Howard Moraff', 'PO_EMAI': None, 'PO_PHON': None}
Current trends in image segmentation indicate that in order to                  achieve connsistently good results, some form of domain knowledge               must be used.  Knowledge-based image-segmentation systems                       traditionally use models of objects and their interrelationships                to guide or  postprocess the low-level segmentation operation.  A               major shortcoming of that appproach is the size and complexity of               the knowledge base needed for all but trivial domains.  The                     knowledge is often quite difficult to embed in the system and                   usually is not transferred easily to other domains.  Also,                      processing time can be large.                                                                                                                                   The goal of the proposed work is to explore the use of a small                  set of domain constraints, namely lighting, camera, and general                 object surface characteristics, to produce acceptable                           segmentation results without the need to classify objects                       beforehand.  As images can be constructed graphically using only                surface orientation, color, and reflectivity, along with the                    position and type of lighting, it is hypothesized that general                  domain information will often be sufficient to perform adequate                 segmentation.  The proposed system will use this knowledge to                   drive a low-level expert segmentation system, determining                       parameters, selecting strategies, and resolving ambiguous                       interpretations.
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{'Code': '6840', 'Text': 'ROBOTICS'}
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