Proceedings of the 5th international conference on Multimodal interfaces
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
On scene interpretation with description logics
Image and Vision Computing
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Context-aware classification for incremental scene interpretation
Proceedings of the Workshop on Use of Context in Vision Processing
S-SEER: selective perception in a multimodal office activity recognition system
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
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A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision-making are central issues for selective vision, which takes advantage of prior knowledge of a domain''s abstract and geometrical structure and models for the expected performance and cost of visual operators. .pp The TEA-1 selective vision system uses Bayes nets for representation and benefit-cost analysis for control of visual and non-visual actions. It is the high-level control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA-1 demonstrates that Bayes nets and decision theoretic techniques provide a general, re-usable framework for constructing computer vision systems that are selective perception systems, and that Bayes nets provide a general framework for representing visual tasks. Control, or decision making, is the most important issue in a selective vision system. TEA-1''s decisions about what to do next are based on general hand-crafted ``goodness functions'''' constructed around core decision theoretic elements. Several goodness functions for different decisions are presented and evaluated. .pp The TEA-1 system solves a version of the T-world problem, an abstraction of a large set of domains and tasks. Some key factors that affect the success of selective perception are analyzed by examining how each factor affects the overall performance of TEA-1 when solving ensembles of randomly produced, simulated T-world domains and tasks. TEA-1''s decision making algorithms are also evaluated in this manner. Experiments in the lab for one specific T-world domain, table settings, are also presented.