Direct feature detection on compressed images

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We present a general approach to the development of adaptive image processing operators of variable shape and size that can be applied directly to non-uniformly sampled images. The approach is demonstrated for second order derivative operators using content-based compressed images.
LanguageEnglish
Pages2336-2345
JournalPattern Recognition Letters
Volume26
Issue number15
DOIs
Publication statusPublished - 1 Nov 2005

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Image processing
Derivatives

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title = "Direct feature detection on compressed images",
abstract = "We present a general approach to the development of adaptive image processing operators of variable shape and size that can be applied directly to non-uniformly sampled images. The approach is demonstrated for second order derivative operators using content-based compressed images.",
author = "BW Scotney and SA Coleman and MG Herron",
note = "Practical situations arise in which image data may be sparse and irregular in location: through incomplete sensing or compression techniques. Subsequent image processing usually requires image reconstruction/interpolation, which is computationally expensive and approximate. This paper pioneers direct processing of incomplete/compressed image data without image reconstruction. The work builds on output in IEEE ICIP 2002 and 2003; has been extended in IEEE ICIP 2005 and 2006; has led to exchange of research resources (algorithm code) with one of the pioneers of mesh modeling compression techniques (Professor Yang, Illinois Institute of Technology); and is being developed in the EPSRC-funded project EP/C006283/1.",
year = "2005",
month = "11",
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language = "English",
volume = "26",
pages = "2336--2345",
journal = "Pattern Recognition Letters",
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Direct feature detection on compressed images. / Scotney, BW; Coleman, SA; Herron, MG.

In: Pattern Recognition Letters, Vol. 26, No. 15, 01.11.2005, p. 2336-2345.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Direct feature detection on compressed images

AU - Scotney, BW

AU - Coleman, SA

AU - Herron, MG

N1 - Practical situations arise in which image data may be sparse and irregular in location: through incomplete sensing or compression techniques. Subsequent image processing usually requires image reconstruction/interpolation, which is computationally expensive and approximate. This paper pioneers direct processing of incomplete/compressed image data without image reconstruction. The work builds on output in IEEE ICIP 2002 and 2003; has been extended in IEEE ICIP 2005 and 2006; has led to exchange of research resources (algorithm code) with one of the pioneers of mesh modeling compression techniques (Professor Yang, Illinois Institute of Technology); and is being developed in the EPSRC-funded project EP/C006283/1.

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AB - We present a general approach to the development of adaptive image processing operators of variable shape and size that can be applied directly to non-uniformly sampled images. The approach is demonstrated for second order derivative operators using content-based compressed images.

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M3 - Article

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EP - 2345

JO - Pattern Recognition Letters

T2 - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

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