NDLI: Fitting a Model to Noisy Data Using Low-Order Implicit Curves and Surfaces

Content Provider	IEEE Xplore Digital Library
Author	Hunyadi, L. Vajk, I.
Copyright Year	2011
Abstract	Fitting a compact model to measured data that captures the underlying relationship is a fundamental task in computer graphics and computer-aided design. Low-order implicit curves and surfaces are a practical choice in grasping this relationship since they are closed under several geometric operations (e.g. intersection, union, offset) while they offer a higher degree of smoothness than their parametric counterparts, and may be preferred especially if the object under study itself is a composition of geometric shapes. We present a method based on a blend of iterative maximum likelihood approximation of linear and quadratic curves and surfaces (with constraints), and of an alternating optimization scheme in the flavor of the standard algorithm for k-means. The algorithm alternates between two steps: (1) fitting a set of linear and quadratic curves and surfaces to previously identified groups of noisy data points, and (2) identifying new groups by assignment to the most feasible shape. Non-iterative direct methods are proposed to seed the maximum likelihood estimator with initial parameter values.
Starting Page	106
Ending Page	114
File Size	486273
Page Count	9
File Format	PDF
ISBN	9781457706837
DOI	10.1109/ECBS-EERC.2011.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-05
Publisher Place	Slovakia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Shape Implicit curves and surfaces Noise Clustering algorithms Model reconstruction Data models Polynomials Approximation methods Alternating optimization Spline
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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