Biologically inspired means for rank-order encoding images: a quantitative analysis

Basabdatta Sen Bhattacharya, Stephen B Furber

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present biologically inspired means to enhance perceptually important information retrieval from rank-order encoded images. Validating a retinal model proposed by VanRullen and Thorpe, we observe that on average only up to 70% of the available information can be retrieved from rank-order encoded images. We propose a biologically inspired treatment to reduce losses due to a high correlation of adjacent basis vectors and introduce a filter-overlap correction algorithm (FoCal) based on the lateral inhibition technique used by sensory neurons to deal with data redundancy. We observe a more than 10% increase in perceptually important information recovery. Subsequently, we present a model of the primate retinal ganglion cell layout corresponding to the foveal-pit. We observe that information recovery using the foveal-pit model is possible only if FoCal is used in tandem. Furthermore, information recovery is similar for both the foveal-pit model and VanRullen and Thorpe's retinal model when used with FoCal. This is in spite of the fact that the foveal-pit model has four ganglion cell layers as in biology while VanRullen and Thorpe's retinal model has a 16-layer structure. © 2006 IEEE.
Original languageEnglish
Article number5484611
Pages (from-to)1087-1099
Number of pages13
JournalIEEE Transactions on Neural Networks
Volume21
Issue number7
DOIs
Publication statusPublished - Jul 2010

Keywords

  • Algorithms
  • Animals
  • Diagnostic Imaging
  • Fovea Centralis
  • Image Processing, Computer-Assisted
  • Information Storage and Retrieval
  • Models, Neurological
  • Neural Inhibition
  • Neural Networks (Computer)
  • Primates
  • Reproducibility of Results
  • Retina
  • Retinal Ganglion Cells
  • Visual Pathways
  • Journal Article

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