Direct feature detection on compressed images

BW Scotney; SA Coleman; MG Herron

doi:10.1016/j.patrec.2005.04.006

Direct feature detection on compressed images

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

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.

Original language	English
Pages (from-to)	2336-2345
Journal	Pattern Recognition Letters
Volume	26
Issue number	15
DOIs	https://doi.org/10.1016/j.patrec.2005.04.006
Publication status	Published (in print/issue) - 1 Nov 2005

Bibliographical 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.

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10.1016/j.patrec.2005.04.006

Cite this

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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",

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doi = "10.1016/j.patrec.2005.04.006",

language = "English",

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AU - Coleman, SA

AU - Herron, MG

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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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DO - 10.1016/j.patrec.2005.04.006

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VL - 26

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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Direct feature detection on compressed images

Abstract

Bibliographical note

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