Filtering with Gray-Code Kernels

Authors:
Gil Ben-Artzi;Hagit Hel-Or;Yacov Hel-Or
Affiliations:
Bar-Ilan University, Ramat Gan, Israel;University of Haifa, Haifa, Israel;The Interdisciplinary Center, Herzliya, Israel
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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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 2 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 amongst others. The GCK can also be used to approximate arbitrary kernels since a sequence of GCK can form 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, and more.