The gray-code filter kernels

Gil Ben-Artzi, Hagit Hel-Or, Yacov Hel-Or

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

In this paper, we introduce a family of filter kernels - the Gray-Code Kernels (GCK) and demonstrate their use in image analysis. Filtering an image with a sequence of Gray-Code Kernels is highly efficient and requires only two operations per pixel for each filter kernel, independent of the size or dimension of the kernel. We show that the family of kernels is large and includes the Walsh-Hadamard kernels, among others. The GCK can be used to approximate any desired kernel and, as such forms, a complete representation. The efficiency of computation using a sequence of GCK filters can be exploited for various real-time applications, such as, pattern detection, feature extraction, texture analysis, texture synthesis, and more.

Original languageEnglish
Pages (from-to)382-393
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number3
DOIs
StatePublished - Mar 2007
Externally publishedYes

Keywords

  • Block matching
  • Convolution
  • Filter kernels
  • Filters
  • Image filtering
  • Pattern detection
  • Pattern matching
  • Walsh-Hadamard

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