Determining Gabor-filter parameters for texture segmentation

Dennis F. Dunn, William E. Higgins, Joseph Wakeley

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

9 Scopus citations


The ability to segment a textured image into separate regions (texture segmentation) continues to be a challenging problem in computer vision. Many texture-segmentation schemes are based on a filter-bank model, where the filters (henceforth referred to as Gabor Filters) are derived from Gabor elementary functions. The goal of these methods is to transform texture differences into detectable filter-output discontinuities at texture boundaries. Then, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the parameters defining the Gabor filters are suitably chosen. Some previous analysis has shown how to design appropriate filters for discriminating simple textures. Designing filters for more general textures, though, has largely been done ad hoc. We have devised a new, more effective, more rigorously based method for determining Gabor-filter parameters. The method is based on an exhaustive, but efficient, search of Gabor-filter parameter space and on a detection-theory formulation of a Gabor filter's output. We provide qualitative arguments and experimental results indicating that our new method is more effective than other methods in producing suitable filter parameters. We demonstrate that our model also gives good filter designs for a variety of texture types.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Number of pages13
ISBN (Print)0819410276
StatePublished - Jan 1 1993
EventIntelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods - Boston, MA, USA
Duration: Nov 18 1992Nov 20 1992

Publication series

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


OtherIntelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods
CityBoston, MA, USA

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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