A Computational Infrastructure Model for Research on Computer Vision

• Antonio Nazaré Júnior UFMG
• Marco Rodrigues UFMG
• Victor Melo UFMG
• Arthur Correia UFMG
• Gabriel Gonçalves UFMG
• Keiller Nogueira UFMG
• Carlos Caetano UFMG
• Edemir Ferreira Júnior UFMG
• Jeferson dos Santos UFMG
• William Schwartz UFMG

DOI: https://doi.org/10.5753/bresci.2015.7203

Resumo

With the advances on science, a powerful computational infrastructure is desirable to increase the performance of experiments. The same holds true for research on the Computer Vision field, which deals with large amounts of data and also requires intensive computation. Nevertheless, the administration of a computational infrastructure involves many tasks, such as system configuration, preventive maintenance and storage management, which becomes very challenging for many research groups. With that in mind, this work proposes an infrastructure model to assist researchers with focus on Computer Vision. We conducted a series of tests to evaluate the performance of our model and the processing power of our infrastructure.

Referências

Haralick, R. (1989). Methodology for experimental computer vision. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR 1989), pages 437–438.

Hasenstein, M. (2001). The logical volume manager (lvm). White paper. Kamath, V. P., Brown, C. S., Pence, J. B., Shekaran, M. C., Lorimor, T. G., Firman, T. R., Gentile, E. J., and Toussaint, K. M. (2004). Extended file system. US Patent 6,754,696.

Patterson, D. A., Gibson, G., and Katz, R. H. (1988). A case for redundant arrays of inexpensive disks (RAID), volume 17. ACM.

Preston, C. (2007). Backup & recovery: inexpensive backup solutions for open systems. ”O’Reilly Media, Inc.”.

Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer-Verlag New York, Inc., 1st edition.

Wong, D., Shih, C.-c., Cao, J., and Dai, W. (2002). Load balancing in link aggregation and trunking. US Patent 6,363,077.