Abstract
The integral histogram is a recently proposed preprocessing technique to compute histograms of arbitrary rectangular gridded (i.e. image or volume) regions in constant time. We formulate a general parallel version of the the integral histogram and analyse its implementation in Star Superscalar (StarSs). StarSs provides a uniform programming and runtime environment and facilitates the development of portable code for heterogeneous parallel architectures. In particular, we discuss the implementation for the multi-core IBM Cell Broadband Engine (Cell/B.E.) and provide extensive performance measurements and tradeoffs using two different scan orders or histogram propagation methods. For 640×480 images, a tile or block size of 28×28 and 16 histogram bins the parallel algorithm is able to reach greater than real-time performance of more than 200 frames per second.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aldavert, D., de Mantaras, R.L., Ramisa, A., Toledo, R.: Fast and robust object segmentation with the integral linear classifier. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 1046–1053 (2010)
Wei, Y., Tao, L.: Efficient histogram-based sliding window. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 3003–3010 (2010)
Blake, G., Dreslinski, R.G., Mudge, T.: A survey of multicore processors. IEEE Signal Processing Magazine 26(6), 26–37 (2009)
Lin, D., Huang, X., Nguyen, Q., Blackburn, J., Rodrigues, C., Huang, T., Do, M.N., Patel, S.J., Hwu, W.-M.W.: The parallelization of video processing. IEEE Signal Processing Magazine 26(6), 103–112 (2009)
Shams, R., Sadeghi, P., Kennedy, R., Hartley, R.: A survey of medical image registration on multicore and the GPU. IEEE Signal Processing Magazine 27(2), 50–60 (2010)
Palaniappan, K., Bunyak, F., Kumar, P., Ersoy, I., Jaeger, S., Ganguli, K., Haridas, A., Fraser, J., Rao, R., Seetharaman, G.: Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video. In: 13th Int. Conf. Information Fusion (2010)
Mehta, S., Misra, A., Singhal, A., Kumar, P., Mittal, A., Palaniappan, K.: Parallel implementation of video surveillance algorithms on GPU architectures using CUDA. In: 17th IEEE Int. Conf. Advanced Computing and Communications, ADCOM (2009)
Kumar, P., Palaniappan, K., Mittal, A., Seetharaman, G.: Parallel blob extraction using the multi-core cell processor. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 320–332. Springer, Heidelberg (2009)
Grauer-Gray, S., Kambhamettu, C., Palaniappan, K.: GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction. In: 5th IAPR Workshop on Pattern Recognition in Remote Sensing (ICPR), pp. 1–4 (2008)
Zhou, L., Kambhamettu, C., Goldgof, D., Palaniappan, K., Hasler, A.F.: Tracking non-rigid motion and structure from 2D satellite cloud images without correspondences. IEEE Trans. Pattern Analysis and Machine Intelligence 23(11), 1330–1336 (2001)
Porikli, F.: Integral histogram: A fast way to extract histograms in Cartesian spaces. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 829–836 (2005)
Podlozhnyuk, V.: Image convolution with CUDA. Technical report, NVIDIA Corp., Santa Clara, CA (2007)
Planas, J., Badia, R.M., Ayguadé, E., Labarta, J.: Hierarchical task-based programming with StarSs. Int. J. High Perform. Comput. Appl. 23(3), 284–299 (2009)
Perez, J.M., Bellens, P., Badia, R.M., Labarta, J.: CellSs: Making it easier to program the Cell Broadband Engine processor. IBM J. Res. Dev. 51(5), 593–604 (2007)
Palaniappan, K., Ersoy, I., Nath, S.K.: Moving object segmentation using the flux tensor for biological video microscopy. In: Ip, H.H.-S., Au, O.C., Leung, H., Sun, M.-T., Ma, W.-Y., Hu, S.-M. (eds.) PCM 2007. LNCS, vol. 4810, pp. 483–493. Springer, Heidelberg (2007)
Bunyak, F., Palaniappan, K., Nath, S.K., Seetharaman, G.: Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking. J. Multimedia 2(4), 20–33 (2007)
Bunyak, F., Palaniappan, K., Nath, S.K., Seetharaman, G.: Geodesic active contour based fusion of visible and infrared video for persistent object tracking. In: 8th IEEE Workshop Applications of Computer Vision (WACV 2007), Austin, TX, pp. 35–42 (February 2007)
Chan, A.L.: A description on the second dataset of the U.S. Army Research Laboratory Force Protection Surveillance System. Technical Report ARL-MR-0670, Army Research Laboratory, Adelphi, MD (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bellens, P., Palaniappan, K., Badia, R.M., Seetharaman, G., Labarta, J. (2011). Parallel Implementation of the Integral Histogram. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-23687-7_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
eBook Packages: Computer ScienceComputer Science (R0)