Scale space from nonlinear filters

JA Bangham; PD Ling; RW Harvey

doi:10.1109/ICCV.1995.466791

Scale space from nonlinear filters

JA Bangham, PD Ling, RW Harvey

Research output: Contribution to conference › Other

6 Citations (Scopus)

Abstract

Decomposition by extrema is put into the context of linear vision systems and scale-space. One dimensional discrete M- and N-sieves neither introduce new edges as the scale increases nor create new extrema. They share this property with diffusion based filters. Furthermore M- and N-sieve algorithms are extremely fast with order complexity n. Used to decompose an image, the resulting granularity is appropriate for pattern recognition.

Original language	English
Pages	163-168
Number of pages	6
DOIs	https://doi.org/10.1109/ICCV.1995.466791
Publication status	Published - Jun 1995
Event	5th International Conference on Computer Vision - Cambridge, United States Duration: 20 Jun 1995 → 23 Jun 1995

Conference

Conference	5th International Conference on Computer Vision
Country/Territory	United States
City	Cambridge
Period	20/06/95 → 23/06/95

Access to Document

10.1109/ICCV.1995.466791

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=466791

Cite this

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title = "Scale space from nonlinear filters",

abstract = "Decomposition by extrema is put into the context of linear vision systems and scale-space. One dimensional discrete M- and N-sieves neither introduce new edges as the scale increases nor create new extrema. They share this property with diffusion based filters. Furthermore M- and N-sieve algorithms are extremely fast with order complexity n. Used to decompose an image, the resulting granularity is appropriate for pattern recognition.",

author = "JA Bangham and PD Ling and RW Harvey",

year = "1995",

month = jun,

doi = "10.1109/ICCV.1995.466791",

language = "English",

pages = "163--168",

note = "5th International Conference on Computer Vision ; Conference date: 20-06-1995 Through 23-06-1995",

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AU - Ling, PD

AU - Harvey, RW

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AB - Decomposition by extrema is put into the context of linear vision systems and scale-space. One dimensional discrete M- and N-sieves neither introduce new edges as the scale increases nor create new extrema. They share this property with diffusion based filters. Furthermore M- and N-sieve algorithms are extremely fast with order complexity n. Used to decompose an image, the resulting granularity is appropriate for pattern recognition.

U2 - 10.1109/ICCV.1995.466791

DO - 10.1109/ICCV.1995.466791

M3 - Other

SP - 163

EP - 168

T2 - 5th International Conference on Computer Vision

Y2 - 20 June 1995 through 23 June 1995

ER -