Learning control strategies for object recognition
Symbolic visual learning
Adapting Object Recognition across Domains: A Demonstration
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Statistical learning, localization, and identification of objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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Object recognition systems contain a large amount of highly specific knowledge tailored to the objects in the domain of interest. Not only does the system require information for each object in the recognition process, it may require entirely different vision processing techniques. Generic programming for vision processing tasks is hard since systems on-board a mobile robots have strong performance requirements. Such issues as keeping up with incoming frames from a camera limit the layers of abstraction that can be applied. This results in software that is customized to the domain at hand, that is difficult to port to other applications and that is not particularly robust to changes in the visual environment. In this paper we describe a high level object definition language that removes the domain specific knowledge from the implementation of the object recognition system. The language has features of object-orientation and logic, being more declarative and less imperative. We present an implementation of the language efficient enough to be used on a Sony AIBO in the Robocup Four-Legged league competition and several illustrations of its use to rapidly adjust to new environments through quickly crafted object definitions.