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On Visual Attention in Natural Images
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human eye tracking) using different evaluation methods over three generated dataset of synthetic patterns and natural images. Our results showed that the objective model is partially valid and highly center-biased.By using empirical data obtained from subjective experiments we propose a special function, the Probability of Characteristic Radially Dependency Function, to model the lateral distribution of visual attention process.

Place, publisher, year, edition, pages
2015. , 113 p.
Keyword [en]
Bottom-up attention, eye movement prediction, model comparison, visual attention, visual saliency
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-48256OAI: oai:DiVA.org:lnu-48256DiVA: diva2:878271
Supervisors
Examiners
Available from: 2015-12-16 Created: 2015-12-08 Last updated: 2015-12-16Bibliographically approved

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CiteExportLink to record
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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