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Real-time Machine Vision with GPU-acceleration using Quasar
Jonas De Vylder
Simon Donné
Dirk Van Haerenborgh
Bart Goossens
  DOI :  10.2352/ISSN.2470-1173.2016.14.IPMVA-375  Published Online :  February 2016
Abstract

The computational performance of graphical processing units (GPUs) has improved significantly, achieving even speed-up factors of 10x-50x compared to single-threaded CPU execution are not uncommon. This makes their use for high throughput machine vision very appealing. However, GPU programming is challenging, requiring a significant programming expertise. We present a new programming framework that mitigates the challenges common for GPU programming while maintaining the significant acceleration.

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151 USA
Subject Areas :
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 DOI 10.2352/ISSN.2470-1173.2016.14.IPMVA-375
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Jonas De Vylder, Simon Donné, Dirk Van Haerenborgh, Bart Goossens, "Real-time Machine Vision with GPU-acceleration using Quasar"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Machine Vision Applications IX,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-375

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Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA
10.2352/ISSN.2470-1173.2016.14.IPMVA-375
2470-1173(20160214)2016:14L.1;1-
ei_24701173_v2016n14_input/s5.xml
/ist/ei/2016/00002016/00000014/art00001
Articles
Real-time Machine Vision with GPU-acceleration using Quasar
VylderJonas De
DonnéSimon
HaerenborghDirk Van
GoossensBart
14022016
2016
14
1
2
2016
The computational performance of graphical processing units (GPUs) has improved significantly, achieving even speed-up factors of 10x-50x compared to single-threaded CPU execution are not uncommon. This makes their use for high throughput machine vision very appealing. However, GPU programming is challenging, requiring a significant programming expertise. We present a new programming framework that mitigates the challenges common for GPU programming while maintaining the significant acceleration.