Efficient computation of two-dimensional Gaussian windows

Peter Meer

doi:10.1016/0167-8655(88)90106-7

Efficient computation of two-dimensional Gaussian windows

Peter Meer

School of Engineering, Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Kaiser (1987) described a fast recursive algorithm for computation of equidistant samples of a one-dimensional Gaussian function requiring only two multiplications per sampling point. We show that the algorithm remains valid for two-dimensional Gaussian windows and discuss its application to specific computer vision problems.

Original language	English (US)
Pages (from-to)	227-229
Number of pages	3
Journal	Pattern Recognition Letters
Volume	7
Issue number	4
DOIs	https://doi.org/10.1016/0167-8655(88)90106-7
State	Published - Apr 1988

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

Keywords

Gaussian windows
fast filters
recursive computations

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10.1016/0167-8655(88)90106-7

Cite this

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title = "Efficient computation of two-dimensional Gaussian windows",

abstract = "Kaiser (1987) described a fast recursive algorithm for computation of equidistant samples of a one-dimensional Gaussian function requiring only two multiplications per sampling point. We show that the algorithm remains valid for two-dimensional Gaussian windows and discuss its application to specific computer vision problems.",

keywords = "Gaussian windows, fast filters, recursive computations",

author = "Peter Meer",

note = "Funding Information: The support of the National Science Foundation under Grant DCR-86-03723 is gratefully acknowledged.",

year = "1988",

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doi = "10.1016/0167-8655(88)90106-7",

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AU - Meer, Peter

N1 - Funding Information: The support of the National Science Foundation under Grant DCR-86-03723 is gratefully acknowledged.

PY - 1988/4

Y1 - 1988/4

N2 - Kaiser (1987) described a fast recursive algorithm for computation of equidistant samples of a one-dimensional Gaussian function requiring only two multiplications per sampling point. We show that the algorithm remains valid for two-dimensional Gaussian windows and discuss its application to specific computer vision problems.

AB - Kaiser (1987) described a fast recursive algorithm for computation of equidistant samples of a one-dimensional Gaussian function requiring only two multiplications per sampling point. We show that the algorithm remains valid for two-dimensional Gaussian windows and discuss its application to specific computer vision problems.

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KW - fast filters

KW - recursive computations

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DO - 10.1016/0167-8655(88)90106-7

M3 - Article

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JO - Pattern Recognition Letters

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IS - 4

ER -

Efficient computation of two-dimensional Gaussian windows

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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