Browsing by Author "Ward, Christopher"
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Item Better Global Polynomial Approximation for Image Rectification(2011-06-14) Ward, ChristopherWhen using images to locate objects, there is the problem of correcting for distortion and misalignment in the images. An elegant way of solving this problem is to generate an error correcting function that maps points in an image to their corrected locations. We generate such a function by fitting a polynomial to a set of sample points. The objective is to identify a polynomial that passes "sufficiently close" to these points with "good" approximation of intermediate points. In the past, it has been difficult to achieve good global polynomial approximation using only sample points. We report on the development of a global polynomial approximation algorithm for solving this problem.Item Better Global Polynomial Approximation for Image Rectification(2009-11-10T15:41:40Z) Ward, ChristopherWhen using images to locate objects, there is the problem of correcting for distortion and misalignment in the images. An elegant way of solving this problem is to generate an error correcting function that maps points in an image to their corrected locations. We generate such a function by fitting a polynomial to a set of sample points. The objective is to identify a polynomial that passes "sufficiently close" to these points with "good" approximation of intermediate points. In the past, it has been difficult to achieve good global polynomial approximation using only sample points. We report on the development of a global polynomial approximation algorithm for solving this problem.Item Earthquake classifying neural networks trained with random dynamic neighborhood PSOs(2011-05-26) Mohais, Arvind S.; Mohais, Rosemarie; Ward, Christopher; Posthoff, ChristianThis paper investigates the use of Random Dynamic Neighborhoods in Particle Swarm Optimization (PSO) for the purposeof training fixed-architecture neural networks to classify a real-world data set of seismological data.Instead of the ring or fully-connected neighborhoods that are typically used with PSOs, or even more complex graph structures, this work uses directed graphs that are randomly generated using size and uniform out-degree as parameters. Furthermore, the graphs are subjected to dynamism during the course of a run, thereby allowing for varying information exchange patterns. Neighborhood re-structuring is applied with a linearly decreasing probability at each iteration. Several experimental configurations are tested on a training portion of the data set, and are ranked according to their abilities to generalize over the entire set. Comparisons are performed with standard PSOs as well as several static non-random neighborhoods.Item Earthquake classifying neural networks trained with random dynamic PSOs(GECCO, 2007) Ward, Christopher; Mohais, Arvind S.; Mohais, Rosemarie; Posthoff, Christian