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Successive Recognition using Local State Models

Per-Erik Forssén
SSAB02, Lund
Proceedings SSAB02 Symposium on Image Analysis
Pages 9-12
March 2002


This paper describes how a world model for successive recognition can be learned using associative learning. The learned world model consists of a linear mapping that successively updates a high-dimensional system state using performed actions and observed percepts. The actions of the system are learned by rewarding actions that are good at resolving state ambiguities. As a demonstration, the system is used to resolve the localisation problem in a labyrinth.

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Bibtex entry

  author = 	 {Per-Erik Forss\'en},
  title = 	 {Successive Recognition using Local State Models},
  booktitle = 	 {Proceedings {SSAB02} {S}ymposium on {I}mage {A}nalysis},
  pages = 	 {9--12},
  year = 	 {2002},
  address = 	 {Lund},
  month = 	 {March},
  organization = {SSAB}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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