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Partially Observable Markov Decision Processes for Faster Object Recognition
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Object recognition in the real world is a big challenge in the field of computer vision. Given the potentially enormous size of the search space it is essential to be able to make intelligent decisions about where in the visual field to obtain information from to reduce the computational resources needed.

In this report a POMDP (Partially Observable Markov Decision Process) learning framework, using a policy gradient method and information rewards as a training signal, has been implemented and used to train fixation policies that aim to maximize the information gathered in each fixation. The purpose of such policies is to make object recognition faster by reducing the number of fixations needed. The trained policies are evaluated by simulation and comparing them with several fixed policies. Finally it is shown that it is possible to use the framework to train policies that outperform the fixed policies for certain observation models.

Place, publisher, year, edition, pages
2016.
Keyword [en]
pomdp, policy gradient, optimal control, object detection, computer vision, information rewards, fixation policy, observation model
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-198632OAI: oai:DiVA.org:kth-198632DiVA, id: diva2:1057748
External cooperation
OculusAI Technologies AB
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf