Image Processing on GPU: Application of Integral Image


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Abstract


In this paper we present an integral image algorithm that can run in real-time on a Graphics Processing Unit (GPU). Our system exploits the parallelisms in computation via the NVIDA CUDA programming model, which is a software platform for solving non-graphics problems in a massively parallel high performance fashion. We compare the performance of the parallel approach running on the GPU with the sequential CPU implementation across a range of image sizes
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Keywords


Integral Image; GPU; CPU; NVIDIA CUDA

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References


NVIDIA Corporation, NVIDIA CUDA Compute Unified Device Architecture Programming Guide, Version 1.1, 2007

D John Owens, Mike Houston, David Luebke, GPU Computing, Proceedings of the IEEE, vol. 96, no. 5, May 2008

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. vol. 2781, pp. 297-304, 2003

H Bay, A Ess, T Tuytelaars, L Van Gool, Speededup robust features (SURF), International Journal on Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008

H Bay, T Tuytelaars, L Van Gool, SURF: Speeded up robust features, Proceedings of the European Conference on Computer Vision, Springer LNCS volume 3951, part 1, pp 404–417,2006.

M Agrawal, K Konolige, M R Blas Censure: Center surround extremas for realtime feature detection and matching, In D. A. Forsyth, P. H. S. Torr, and A. Zisserman, editors, ECCV (4), volume 5305 of Lecture Notes in Computer Science, pp 102–115. Springer, 2008.

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, cvprw, pp.9-14, 2009

Pablo Augusto Negri, Détection et Reconnaissance d’objets structurés : Application aux Transports Intelligents, thesis defended in September 2008, University Pierre and Marie Curie - Paris VI, Institute of Intelligent Systems and Robotics.

Lemaire Pierre, Etude de la pertinence topologique des descripteurs d’images utilisés dans les algorithmes de détection de visages par apprentissage, Master course in 2008.

Shuji Zhao, Apprentissage et Recherche par le Contenu Visuel de Catégories Sémantiques d'Objets Vidéo, Master memory supported in July 2007, University Paris Descartes, Laboratory of Image Processing and Signal CNRS France.

P A Negri, L Prévost, X Clady, Cascade generative and discriminative classifiers for vehicle detection, 16 French Congress AFRIFAFIA, Pattern Recognition and Artificial Intelligence, Amiens, France, pp 22-25 January 2008.

Tabatabaie, Z.S., Rahmat, R.W., Udzir, N.I., Kheirkhah, E., Adaptive skin color classification technique for color-based face detection systems using integral image, (2011) International Review on Computers and Software (IRECOS), 6 (1), pp. 32-39.

A. Sadr, Z. Khosravi, A. K. Momtaz, Application of Topographic Independent Component Analysis in Edge Detection and Segmentation of Ultrasonic NDE Images, (2012) International Journal on Communications Antenna and Propagation (IRECAP), 2 (1), pp. 73-79.

H. Zolfaghari, A. S. Nekonam, J. Haddadnia, Computing Geometry Parameters Affecting on Osteoporoses by Image Processing, (mber) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 495-499.


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