García, I.

González-Linares, J. M.

Guil, N.

Ortigosa, Pilar M.

González-Linares J. M.

Ortigosa Pilar M.

Guil N.

García I.

University of Szeged

Parallelization of an algorithm for non rigid objects detection withstochastic global optimizationJ.M. Gonzalez-Linares, P.M. Ortigosa, N. Guil and I. GarciaIn many real applications objects suffer different deformations due to several factors like sam-pling errors or the flexibility of the material. Many algorithms have been developed to recover suchobjects. Some of them are based in the use of free-form deformable models like "snakes". Others arebased in parametric models, where an object matching algorithm, combined with this deformationmodel, is used as a Bayesian objective function that is minimized by a gradient descent method.In this talk, a parallel algorithm for detecting rigid and non-rigid objects in an image will bepresented. This algorithm uses a Bayesian inference scheme to register the parameters that describethe object of interest included in the image. This kind of techniques requires knowledge about theshape of the object. This prior information of the object shape is composed of a prototype template, aset of deformation transformations of the template, and a probability density on the set of deformationtransformations.The likelihood measures the similarity between the deformed template and the objects present inthe image. A Generalized Hough Transform (GHT [1, 2]) is used to detect the deformed templatein the image and to measure the similarity using a defined energy function. The prior informationis combined with the likelihood to obtain the a posteriori probability. Finally, by obtaining themaximum of this probability (Maximum A Posteriori), the deformed template is identified in theimage. This MAP estimate is computed using the evolutionary global optimization algorithm UEGO(stands for Universal Evolutionary Global Optimizer, [3, 4]).Our proposed algorithm is very robust and it is able to detect non-rigid objects even in the pres-ence of noise or occlusions. Unfortunately, it is computationally very expensive. To solve thisdisadvantage a possible parallelization of the algorithm is proposed. The parallel strategy consists ona master-slave configuration. The tasks are distributed among the processors to balance the compu-tational load. It has been implemented using the message-passing paradigm with PVM. The parallelprogram has been tested in a Cray T3E using up to 32 processors.References[1] D.H. Ballard. “Generalizing the Hough Transform to detect Arbitrary Shapes”. Pattern Recog-nition, 14(2), 1981, pp. 111-122.[2] N. Guil, J.M. Gonzalez-Linares, and E.L. Zapata. “Bidimensional shape detection using aninvariant approach”. Pattern Recognition, 32(6), 1999, 1025-1038.[3] P.M. Ortigosa, M. Jelasity, and I. Garcia. “Uego, an evolutionary algorithm for solving globaloptimization problems”. Journal of Heuristics, Kluwer Academic Publishers.[4] M. Jelasity, P.M. Ortigosa and I. Garcia. “Description of two parallel implementations forUEGO algorithm”. Matrahaza, Hungary, March 1999. XIV Intl. Conf. on Mathematical Pro-gramming.38

Parallelization of an algorithm for non rigid objects detection with stochastic global optimization : [abstract]

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Parallelization of an algorithm for non rigid objects detection with stochastic global optimization

Parallelization of an algorithm for non rigid objects detection with stochastic global optimization

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