论文信息 - Integral image computation on GPU

Integral image computation on GPU

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.

[1] Luc Van Gool,et al. Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[2] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3] Mosalam Ebrahimi,et al. SUSurE: Speeded Up Surround Extrema feature detector and descriptor for realtime applications , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[4] Luc Van Gool,et al. SURF: Speeded Up Robust Features , 2006, ECCV.

[5] Pablo Negri. Détection et reconnaissance d'objets structurés : application aux transports intelligents , 2008 .

[6] John D. Owens,et al. GPU Computing , 2008, Proceedings of the IEEE.

[7] L. Prevost,et al. Cascade de classifieurs génératifs et discriminants pour la détection de véhicules A cascade of generative and discriminative classifiers for vehicle detection , 2007 .

[8] Kurt Konolige,et al. CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching , 2008, ECCV.

[9] Rainer Lienhart,et al. Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[10] Lionel Prevost,et al. A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection , 2008, EURASIP J. Adv. Signal Process..