Parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources

Rubén Usamentiaga; Daniel F. García; Julio Molleda; Ignacio Sainz; Francisco G. Bulnes

doi:10.1117/12.872212

25 January 2011 Parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources

Rubén Usamentiaga, Daniel F. García, Julio Molleda, Ignacio Sainz, Francisco G. Bulnes

Author Affiliations +

Proceedings Volume 7872, Parallel Processing for Imaging Applications; 787205 (2011) https://doi.org/10.1117/12.872212
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Advances in the image processing field have brought new methods which are able to perform complex tasks robustly. However, in order to meet constraints on functionality and reliability, imaging application developers often design complex algorithms with many parameters which must be finely tuned for each particular environment. The best approach for tuning these algorithms is to use an automatic training method, but the computational cost of this kind of training method is prohibitive, making it inviable even in powerful machines. The same problem arises when designing testing procedures. This work presents methods to train and test complex image processing algorithms in parallel execution environments. The approach proposed in this work is to use existing resources in offices or laboratories, rather than expensive clusters. These resources are typically non-dedicated, heterogeneous and unreliable. The proposed methods have been designed to deal with all these issues. Two methods are proposed: intelligent training based on genetic algorithms and PVM, and a full factorial design based on grid computing which can be used for training or testing. These methods are capable of harnessing the available computational power resources, giving more work to more powerful machines, while taking its unreliable nature into account. Both methods have been tested using real applications.

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Rubén Usamentiaga, Daniel F. García, Julio Molleda, Ignacio Sainz, and Francisco G. Bulnes "Parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources", Proc. SPIE 7872, Parallel Processing for Imaging Applications, 787205 (25 January 2011); https://doi.org/10.1117/12.872212

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KEYWORDS

Image processing

Genetic algorithms

Detection and tracking algorithms

Edge detection

Algorithm development

Reliability

Optimization (mathematics)

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