Filtering with Gray-Code Kernels

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

doi:10.1109/icpr.2004.1334198

Filtering with Gray-Code Kernels

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

Research output: Contribution to journal › Conference article › peer-review

7 Scopus citations

Abstract

In this paper we introduce a family of filier 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.

Original language	English
Pages (from-to)	556-559
Number of pages	4
Journal	Proceedings - International Conference on Pattern Recognition
Volume	1
DOIs	https://doi.org/10.1109/icpr.2004.1334198
State	Published - 2004
Externally published	Yes
Event	Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom Duration: 23 Aug 2004 → 26 Aug 2004

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abstract = "In this paper we introduce a family of filier 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.",

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AB - In this paper we introduce a family of filier 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.

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Filtering with Gray-Code Kernels

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