Home   >   CSC-OpenAccess Library   >    Manuscript Information
Image Processing Application on Graphics processors
Marwa Chouchene, Bahri Haythem, Sayadi Fatma Ezahra, Atri Mohamed
Pages - 66 - 72     |    Revised - 31-03-2014     |    Published - 30-04-2014
Volume - 8   Issue - 3    |    Publication Date - June 2014  Table of Contents
MORE INFORMATION
KEYWORDS
Image Processing, GPU, CUDA.
ABSTRACT
In this work, we introduce real time image processing techniques using modern programmable Graphic Processing Units GPU. GPU are SIMD (Single Instruction, Multiple Data) device that is inherently data-parallel. By utilizing NVIDIA new GPU programming framework, “Compute Unified Device Architecture” CUDA as a computational resource, we realize significant acceleration in image processing algorithm computations. We show that a range of computer vision algorithms map readily to CUDA with significant performance gains. Specifically, we demonstrate the efficiency of our approach by a parallelization and optimization of image processing, Morphology applications and image integral.
CITED BY (1)  
1 Alqahtani, K. M. (2016). A Graphical Processing Unit Based on Real Time System (Doctoral dissertation).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 TechRepublic 
5 Scribd 
6 SlideShare 
7 PdfSR 
Allard, J. and Raffin, B., "A shader-based parallel rendering framework. in Visualization",2005,VIS 05. IEEE, pp 127-134.
H Bay, A Ess, T Tuytelaars, L Van Gool, "Speededup robust features (SURF) ", International Journal on Computer Vision and Image Understanding, 2008, vol. 110, no. 3, pp. 346–359.
H Bay, T Tuytelaars, L Van Gool, "SURF: Speeded up robust features", Proceedings of the European Conference on Computer Vision, 2006, Springer LNCS volume 3951, part 1, pp 404–417.
L. Abbas-Turki, S. Vialle, B. Lapeyre, and P. Mercier. "High Dimensional Pricing of Exotic European Contracts on a GPU Cluster, and Comparison to a CPU Cluster". In Second InternationalWorkshop on Parallel and Distributed Computing in Finance, May 2009.
M Agrawal, K Konolige, M R Blas "Censure: Center surround extremas for realtime feature detection and matching", 2008, ECCV (4), volume 5305 of Lecture Notes in Computer Science,Springer. pp 102– 115.
M Ebrahimi, W W Mayol-Cuevas, "SUSurE: Speeded Up Surround Extrema feature detector and descriptor for realtime applications", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009, CVPRW, pp.9-14.
NVIDIA Corporation, "NVIDIA CUDA Compute Unified Device Architecture Programming Guide", Version 3, 2013.
NVIDIA, CUDA Programming Guide Version 1.1. 2007, NVIDIA Corporation: Santa Clara,California.
P Viola, M Jones, "Rapid object detection using a boosted cascade of simple features",Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2001.
R Lienhart, A Kuranov, V Pisarevsky, "Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection", Pattern Recognition. 2003, vol. 2781, pp. 297-304.
Miss Marwa Chouchene
Laboratory of Electronics and Microelectronics (EuE) Faculty of Sciences Monastir Monastir, 5000 - Tunisia
ch.marwa.84@gmail.com
Dr. Bahri Haythem
Laboratory of Electronics and Microelectronics (E?E) Faculty of Sciences Monastir Monastir, 5000, Tunisia - Tunisia
Dr. Sayadi Fatma Ezahra
Laboratory of Electronics and Microelectronics (E?E) Faculty of Sciences Monastir Monastir, 5000, Tunisia - Tunisia
Dr. Atri Mohamed
Laboratory of Electronics and Microelectronics (E?E) Faculty of Sciences Monastir Monastir, 5000, Tunisia - Tunisia


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS