Unwrapping Hartmann-Shack Images of Off-Axis Aberration using Artificial Centroid Injection Method

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dc.contributor.author Yuwono, Mitchell en_US
dc.contributor.editor Ding, Y en_US
dc.date.accessioned 2012-10-12T03:36:31Z
dc.date.available 2012-10-12T03:36:31Z
dc.date.issued 2011 en_US
dc.identifier 2011004345 en_US
dc.identifier.citation Yuwono Mitchell 2011, 'Unwrapping Hartmann-Shack Images of Off-Axis Aberration using Artificial Centroid Injection Method', , IEEE, Piscataway, USA, , pp. 560-564. en_US
dc.identifier.issn 978-1-4244-9351-7 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19271
dc.description.abstract As the degree of aberration and noise increases, particularly for off-axis aberration, wavefronts of Hartmann-Shack images become so distorted that special care needs to be considered in order to successfully and gracefully unwrap the images. This paper proposes an alternative algorithmic approach called the artificial centroid injection method. Initial centroid extraction is done using Laplacian of Gaussian (LoG) and dynamic thresholding. Outlier centroids are filtered using ensemble of weak classifiers boosted with Adaboost algorithm. Observing the nature of the vertical and horizontal centroid sequences using Kalman Filter, multiple General Regression Neural Networks (GRNN) are then trained to approximate centroid sequences. Artificial centroids are generated by taking the intersection points of approximated vertical and horizontal GRNN sequences that occurs inside an elliptical Region of Interest optimized with Regrouping Particle Swarm (RegPSO). These artificial centroids are injected to the intial centroid vector to predictively recover missing and previously unrecognized spots. Wavefront algorithm is then applied to correspond detected centroids to their appropriate lenslet centers. This algorithm has successfully unwrapped 29 different off-axis aberration HS images, -50A? Temporal plane to +50A? Nasal plane up to zero pixel prediction error, with no false correlations in any of the tested images. en_US
dc.language en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/BMEI.2011.6098352 en_US
dc.title Unwrapping Hartmann-Shack Images of Off-Axis Aberration using Artificial Centroid Injection Method en_US
dc.parent 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 560 en_US
dc.identifier.endpage 564 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 090300 en_US
dc.personcode 11006881 en_US
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Biomedical Engineering and Informatics (BMEI) en_US
dc.date.activity 20111015 en_US
dc.location.activity Shanghai, China en_US
dc.description.keywords Adaboost algorithm , GRNN , Hartmann-Shack image unwrapping , Kalman filter , Laplacian of Gaussian thresholding , RegPSO , artificial centroid injection method , centroid sequences , dynamic thresholding , multiple general regression neural networks , noise , off-axis aberration , regrouping particle swarm optimisation , wavefront algorithm en_US
dc.staffid en_US
dc.staffid 112628 en_US


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