ERNEST: A Semantic Network System for Pattern Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
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Active vision
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Modeling and calibration of automated zoom lenses
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International Journal of Computer Vision
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
Semantic networks for understanding scenes
Semantic networks for understanding scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
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CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Real-Time Pedestrian Tracking in Natural Scenes
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Schritthaltende hybride Objektdetektion
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ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
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ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
MOBSY: Integration of Vision and Dialogue in Service Robots
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
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We present a modular architecture for image understanding and active computer vision which consists of three major components: Sensor and actor interfaces required for data-driven active vision are encapsulated to hide machine-dependent parts; image segmentation is implemented in object-oriented programming as a hierarchy of image operator classes, guaranteeing simple and uniform interfaces; knowledge about the environment is represented either as a semantic network or as statistical object models or as a combination of both; the semantic network formalism is used to represent actions which are needed in explorative vision. We apply these modules to create two application systems. The emphasis here is object localization and recognition in an office room: an active purposive camera control is applied to recover depth information and to focus on interesting objects; color segmentation is used to compute object features which are relatively insensitive to small aspect changes. Object hypotheses are verified by an A*-based search using the knowledge base.