Finding Representative Points of Closest Approach for Noisy Curves (Classic Reprint) - Softcover

Inhaltsangabe

This book explores a novel approach to object recognition by describing objects in terms of their 3D curves. It presents two methods for choosing representative points of closest approach that can be used to efficiently match sets of curves in 3D space, even when the curves are corrupted by noise. The methods are evaluated using computer-generated curves with varying amounts of noise, and the results demonstrate that the centroid method allows better selection of points than quadratic or cubic fits when substantial lengths of the curves can be used, but that a cubic fit of coordinates vs arc length gave better results when relatively short lengths of curve were used. The quadratic fits behaved very badly. The book provides a valuable contribution to the field of object recognition and has applications in data reduction, efficient recognition of 3D objects, and other areas where measuring the spatial separation of sets of curves in 3D space is important.

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