By Enni S.
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Extra resources for A 1-(S,T)-edge-connectivity augmentation algorithm
GAs are computational models of natural evolution in which stronger individuals are more likely to be the winners in a competitive environment. Besides their intrinsic parallelism, GAs are simple and efficient techniques for optimization and search. The main advantage of the GA approach for range image registration is that pre-alignment between views is not necessary to guarantee a good result. However, the GA is a stochastic method and generally time-consuming. GAs have been applied to image registration problems in several areas, including remote sensing [Chalermwat and El-Ghazawi (1999)] and medical imaging [Ahmed et al.
Uniform: Decides, with some probability, which parent will contribute each of the gene values in the offspring chromosomes allowing the parent chromosomes to be mixed at the gene level rather than at the segment level. The mutation operator is an important part of the GAs to prevent the population from stagnating at local optima. Mutation also occurs according to a user-defined probability, usually set fairly low, and there are different 48 Robust Range Image Registration using GAs and the SIM mutation rules.
First, an initial alignment is estimated by a traditional GA followed by ICP refinement to obtain a final registration. Their results show that, in some cases, this method becomes stuck in a local optimum; the authors concede the need for some modifications to their approach. Recently, an alternative range image registration algorithm based on a GA was proposed [Robertson and Fisher (2002)] using a parallel evolutionary registration approach. The experimental results show that the method better avoids premature convergence.
A 1-(S,T)-edge-connectivity augmentation algorithm by Enni S.