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Optimizing and Reconstruction of SAR Images Using Glowworm Swarm Optimization (GSO)?

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 9)

Publication Date:

Authors : ;

Page : 219-230

Keywords : Image Processing; Image segmentation; Expectation Maximization; Glowworm swarm Optimization (GSO);

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Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In the existing System expectation?maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. The glowworm swarm optimization (GSO) is a swarm intelligence optimization algorithm developed based on the behavior of glowworms (also known as fireflies or lightning bugs). The behavior pattern of glowworms which is used for this algorithm is the apparent capability of the glowworms to change the intensity of the luciferin emission and thus appear to glow at different intensities. The GSO algorithm makes the agents glow at intensities approximately proportional to the function value being optimized. The second significant part of the algorithm incorporates a dynamic decision range by which the effect of distant glowworms are discounted when a glowworm has sufficient number of neighbors or the range goes beyond the range of perception of the glowworms.

Last modified: 2014-09-15 23:25:19