Autonomous Operators for Direct use on Irregular Image Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Standard image processing algorithms for digital images require the availability of complete, and regularly sampled, image data. This means that irregular image data must undergo reconstruction to yield regular images to which the algorithms are then applied. The more successful image reconstruction techniques tend to be expensive to implement. Other simpler techniques, such as image interpolation, whilst cheaper, are usually not adequate to support subsequent reliable image processing. This paper presents a family of autonomous image processing operators constructed using the finite element framework that enable direct processing of irregular image data without the need for image reconstruction. The successful use of reduced data (as little as 10% of the original image) affords rapid, accurate, reliable, and computationally inexpensive image processing techniques.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages296-303
Number of pages8
VolumeLNCS 3
DOIs
Publication statusPublished - Sep 2005
EventInternational Conference on Image Analysis and Processing (ICIAP 2005) - Cagliari, Sardinia
Duration: 1 Sep 2005 → …

Conference

ConferenceInternational Conference on Image Analysis and Processing (ICIAP 2005)
Period1/09/05 → …

Fingerprint

Image processing
Image reconstruction
Interpolation
Availability
Processing

Keywords

  • feature extraction
  • irregular image data
  • autonomous operators

Cite this

Coleman, SA ; Scotney, BW. / Autonomous Operators for Direct use on Irregular Image Data. Unknown Host Publication. Vol. LNCS 3 2005. pp. 296-303
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Coleman, SA & Scotney, BW 2005, Autonomous Operators for Direct use on Irregular Image Data. in Unknown Host Publication. vol. LNCS 3, pp. 296-303, International Conference on Image Analysis and Processing (ICIAP 2005), 1/09/05. https://doi.org/10.1007/11553595_36

Autonomous Operators for Direct use on Irregular Image Data. / Coleman, SA; Scotney, BW.

Unknown Host Publication. Vol. LNCS 3 2005. p. 296-303.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Standard image processing algorithms for digital images require the availability of complete, and regularly sampled, image data. This means that irregular image data must undergo reconstruction to yield regular images to which the algorithms are then applied. The more successful image reconstruction techniques tend to be expensive to implement. Other simpler techniques, such as image interpolation, whilst cheaper, are usually not adequate to support subsequent reliable image processing. This paper presents a family of autonomous image processing operators constructed using the finite element framework that enable direct processing of irregular image data without the need for image reconstruction. The successful use of reduced data (as little as 10% of the original image) affords rapid, accurate, reliable, and computationally inexpensive image processing techniques.

AB - Standard image processing algorithms for digital images require the availability of complete, and regularly sampled, image data. This means that irregular image data must undergo reconstruction to yield regular images to which the algorithms are then applied. The more successful image reconstruction techniques tend to be expensive to implement. Other simpler techniques, such as image interpolation, whilst cheaper, are usually not adequate to support subsequent reliable image processing. This paper presents a family of autonomous image processing operators constructed using the finite element framework that enable direct processing of irregular image data without the need for image reconstruction. The successful use of reduced data (as little as 10% of the original image) affords rapid, accurate, reliable, and computationally inexpensive image processing techniques.

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