Hypothesis integration in image understanding systems
Computer Vision, Graphics, and Image Processing
Evidence accumulation & flow of control
AI Magazine
ERNEST: A Semantic Network System for Pattern Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A knowledge-based approach to integration of image processing procedures
CVGIP: Image Understanding
Image and brain: the resolution of the imagery debate
Image and brain: the resolution of the imagery debate
Control of selective perception using Bayes nets and decision theory
International Journal of Computer Vision - Special issue on active vision II
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Learning control strategies for object recognition
Symbolic visual learning
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Extraction of Man-Made Objects from Aerial & Space Images
Automatic Extraction of Man-Made Objects from Aerial & Space Images
Active Knowledge-Based Scene Analysis
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Unsupervised Learning of Biologically Plausible Object Recognition Strategies
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Experience in Integrating Image Processing Programs
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Integrating Aspects of Active Vision into a Knowledge-Based System
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A computer system for visual recognition using active knowledge
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Machine learning for adaptive image interpretation
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A self-referential perceptual inference framework for video interpretation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Genetic Programming and Evolvable Machines
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High-level vision systems use object, scene or domain specific knowledge to interpret images. Unfortunately, this knowledge has to be acquired for every domain. This makes it difficult to port systems from one domain to another, and therefore to compare them. Recently, the authors of the ADORE system have claimed that object recognition can be modeled as a Markov decision process, and that domain-specific control strategies can be inferred automatically from training data. In this paper we demonstrate the generality of this approach by porting ADORE to a new domain, where it controls an object recognition system that previously relied on a semantic network.