Foveal scale space generation with the log-polar transform

Aaron D. Long, Ram M. Narayanan, Timothy J. Kane, Terence F. Rice, Michael J. Tauber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Tracking deep-field objects across a wide field of view requires the use of high resolution image sensors. This imposes a burden on processing systems which must detect and extract features in an image. Deep-field objects have limited spatial support within a wide field of view image and accordingly much of the recorded scene contains superfluous information about the environment. This paper explores the generation of a foveal scale space using the log-polar transform. Foveal scale space is the scale space representation of an input scene where the spatial support of the image at each scale increases with scale and the number of pixels remains constant across each slice of scale space. This paper reports the formulation of a transformation consisting of a peripheral region defined by the log-polar transform and a foveal region where resolution is constant. A method for performing diffusion in this domain is shown and the generation of the foveal scale space is presented.

Original languageEnglish (US)
Title of host publicationImage Sensing Technologies
Subtitle of host publicationMaterials, Devices, Systems, and Applications IV
EditorsAchyut K. Dutta, Nibir K. Dhar
PublisherSPIE
ISBN (Electronic)9781510609198
DOIs
StatePublished - 2017
EventImage Sensing Technologies: Materials, Devices, Systems, and Applications IV 2017 - Anaheim, United States
Duration: Apr 12 2017Apr 13 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10209
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherImage Sensing Technologies: Materials, Devices, Systems, and Applications IV 2017
CountryUnited States
CityAnaheim
Period4/12/174/13/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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