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Fast Algorithm for Walsh Hadamard Transform on Sliding Windows

This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing f...

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Published in:	IEEE transactions on pattern analysis and machine intelligence 2010-01, Vol.32 (1), p.165-171
Main Authors:	Wanli Ouyang, Cham, W.K.
Format:	Article
Language:	English
Subjects:	Application software Applied sciences Artificial intelligence Computer science control theory systems Computer vision Exact sciences and technology Fast algorithm Feature extraction Kernel Manufacturing Pattern matching Pattern recognition. Digital image processing. Computational geometry Signal processing algorithms template matching Tree data structures Video compression Video signal processing Walsh Hadamard Transform
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container_title	IEEE transactions on pattern analysis and machine intelligence
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creator	Wanli Ouyang Cham, W.K.
description	This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding windows.
doi_str_mv	10.1109/TPAMI.2009.104
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source	IEEE Electronic Library (IEL) Journals
subjects	Application software Applied sciences Artificial intelligence Computer science control theory systems Computer vision Exact sciences and technology Fast algorithm Feature extraction Kernel Manufacturing Pattern matching Pattern recognition. Digital image processing. Computational geometry Signal processing algorithms template matching Tree data structures Video compression Video signal processing Walsh Hadamard Transform
title	Fast Algorithm for Walsh Hadamard Transform on Sliding Windows
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