Efficient image processing with the apply language

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Abstract

Apply is a Domain-Specific Language for image processing and low-level computer vision. Apply allows programmers to write kernel operations that focus on the computation for a single pixel location. The compiler generates code to perform the kernel computation over entire images. The original Apply implementation was developed 20 years ago for efficient processing on parallel architectures. The current-generation Apply compiler targets efficient code generation for general-purpose computers, typically outperforming handwritten code, while maintaining the simplicity of the original language. The use of modem compiler writing tools, specifically Stratego/XT, has facilitated improvements in the language design and made it easy to target the compiler to different environments. A large number of computer vision and image processing operations can be expressed in Apply. However, some algorithms require additional features. To motivate future language development, we analyse the requirements of the algorithms provided in a commercial machine vision library.

Original languageEnglish
Title of host publicationProceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007
EditorsM. J. Bottema, A. Maeder, N. Redding, A. van der Hengel
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages533-540
Number of pages8
ISBN (Print)0769530672, 9780769530673
DOIs
Publication statusPublished - 2007
EventAustralian Pattern Recognition Society (APRS) - Glenelg, SA, Australia
Duration: 3 Dec 20075 Dec 2007

Other

OtherAustralian Pattern Recognition Society (APRS)
Country/TerritoryAustralia
CityGlenelg, SA
Period3/12/075/12/07

Bibliographical note

Copyright 2007 IEEE. Reprinted from Proceedings of the 9th biennial conference of the Australian pattern recognition society (2007) : digital image computing techniques and applications (DICTA 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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