Abstract
A parametric method to carry out fringe pattern demodulation by means of a particle swarm optimization is presented. The phase is approximated by the parametric estimation of an nth-grade polynomial so that no further unwrapping is required. On the other hand, a different parametric function can be chosen according to the prior knowledge of the phase behavior. A particle swarm is codified with the parameters of the function that estimates the phase. A fitness function is established to evaluate the particles, which considers: (a) the closeness between the observed fringes and the recovered fringes, (b) the phase smoothness, (c) the prior knowledge of the object as its shape and size. The swarm of particles evolves until a fitness average threshold is obtained. The method was able to successfully demodulate noisy fringe patterns and even a one-image closed-fringe pattern.
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Jimenez, J.F., Cuevas, F.J., Sossa, J.H., Gomez, L.E. (2010). A Parametric Method Applied to Phase Recovery from a Fringe Pattern Based on a Particle Swarm Optimization. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_5
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DOI: https://doi.org/10.1007/978-3-642-13769-3_5
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