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Practical Genericity: Writing Image Processing Algorithms Both Reusable and Efficient

Abstract : An important topic for the image processing and pattern recognition community is the construction of open source and efficient libraries. An increasing number of software frameworks are said to be generic: they allow users to write reusable algorithms compatible with many input image types. However, this design choice is often made at the expense of performance. We present an approach to preserve efficiency in a generic image processing framework, by leveraging data types features. Variants of generic algorithms taking advantage of image types properties can be defined, offering an adjustable trade-off between genericity and efficiency. Our experiments show that these generic optimizations can match dedicated code in terms of execution times, and even sometimes perform better than routines optimized by hand.
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Contributor : Laurent Najman <>
Submitted on : Thursday, November 13, 2014 - 11:55:02 AM
Last modification on : Wednesday, June 9, 2021 - 5:28:03 PM
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Roland Levillain, Thierry Géraud, Laurent Najman, Edwin Carlinet. Practical Genericity: Writing Image Processing Algorithms Both Reusable and Efficient. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Bayro-Corrochano, Eduardo and Hancock, Edwin, Nov 2014, Puerto Vallarta, Mexico. pp.70 - 79, ⟨10.1007/978-3-319-12568-8_9⟩. ⟨hal-01082349⟩



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