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Applications of PNC in Numerical Methods

Applications of PNC in Numerical Methods

Copyright: © 2017 |Pages: 34
ISBN13: 9781522525318|ISBN10: 1522525319|EISBN13: 9781522525325
DOI: 10.4018/978-1-5225-2531-8.ch007
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MLA

Dariusz Jacek Jakóbczak. "Applications of PNC in Numerical Methods." Probabilistic Nodes Combination (PNC) for Object Modeling and Contour Reconstruction, IGI Global, 2017, pp.235-268. https://doi.org/10.4018/978-1-5225-2531-8.ch007

APA

D. Jakóbczak (2017). Applications of PNC in Numerical Methods. IGI Global. https://doi.org/10.4018/978-1-5225-2531-8.ch007

Chicago

Dariusz Jacek Jakóbczak. "Applications of PNC in Numerical Methods." In Probabilistic Nodes Combination (PNC) for Object Modeling and Contour Reconstruction. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2531-8.ch007

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Abstract

Nodes are treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. Two-dimensional object is extrapolated and interpolated via nodes combination and different functions as discrete or continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. Curve interpolation represents one of the most important problems in mathematics and computer science: how to model the curve via discrete set of two-dimensional points? Also the matter of shape representation (as closed curve - contour) and curve parameterization is still opened. For example pattern recognition, signature verification or handwriting identification problems are based on curve modeling via the choice of key points. So interpolation is not only a pure mathematical problem but important task in computer vision and artificial intelligence.

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