Abstract
Highly regular multi-processor architecture are suitable for inherently highly parallelizable applications such as most of the image processing domain. System on a programmable chip (SoPC) allows hardware designers to tailor every aspects of the architecture in order to match the specific application needs. These platforms are now large enough to embed an increasing number of core, allowing implementation of a multi-processor architecture with an embedded communication network.
In this paper we present the parallelization and the embedding of a real time image stabilization algorithm on SoPC platform. Our overall hardware implementation method is based upon meeting algorithm processing power requirement and communication needs with refinement of a generic parallel architecture model. Actual implementation is done by the choice and parameterization of readily available reconfigurable hardware modules and customizable commercially available IPs. We present both software and hardware implementation with performance results on a Xilinx SoPC target.
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Keywords
- Integral Image
- Normalize Cross Correlation
- Homogeneous Transformation Matrix
- Global Movement Estimation
- Global Motion Parameter
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References
Derutin, J.P., et al.: Simd, smp and mimd-dm parallel approaches for real-time 2d image stabilization. In: CAMP 2005. Computer Architecture for Machine Perception, pp. 73–80. IEEE Computer Society, Los Alamitos (2005)
Morimoto, C.: Electronic Digital Stabilization: Design and Evaluation, with Applications. Phd thesis, University of Maryland (1997)
Duric, Z., et al.: Shooting a smooth video with a shaky camera. Machine Vision and Applications 13, 303–313 (2003)
Zhu, Z., et al.: Camera stabilisation based on 2.5d motion estimation and inertial motion filtering. In: International Conference on Intelligent Vehicles (1998)
Horn, B., et al.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)
Barron, J., et al.: Performance of optical flow techniques. International Journal of Computer Vision 12, 43–77 (1994)
Verri, A., et al.: Motion field and optical flow: Qualitative properties. IEEE Trans. Pattern Analysis and Machine Intelligence 11(8), 490–498 (1989)
Harris, C., et al.: A combined corner and edge detector. In: Proceeding of the 4th Alvey Vision Conference, pp. 147–151 (1988)
Pourreza, H., et al.: Weighted multiple bit-plane matching, a simple and efficient matching criterion for electronic digital image stabilizer application. In: 6th International Conference on Signal Processing, vol. 2, pp. 957–960 (2002)
Tsai, D., et al.: The evaluation of normalized cross correlations for defect detection. Pattern Recognition Letters 24, 2525–2535 (2003)
Viola, P., et al.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (2001)
AVNET: Xilinx virtex-4 lx evaluation kit (ADS-XLX-V4LX-EVL60-G) (2008), http://www.em.avnet.com
Mateos, R., et al.: Hardware/software co-simulation environment for csoc with soft processors. In: IEEE Internacional Conference on Field-Programmable Technology ICFPT 2004, pp. 109–114 (2004)
Craven, S., et al.: Configurable soft processor arrays using the openfire processor. In: Proceedings of 2005 MAPLD International Conference, pp. 250–256 (2005)
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© 2008 Springer-Verlag Berlin Heidelberg
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Dérutin, J.P., Damez, L., Landrault, A. (2008). Embedding of a Real Time Image Stabilization Algorithm on SoPC Platform, a Chip Multi-processor Approach. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_15
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DOI: https://doi.org/10.1007/978-3-540-88458-3_15
Publisher Name: Springer, Berlin, Heidelberg
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