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What is Data Extrapolation

Analyzing Data Through Probabilistic Modeling in Statistics
Calculation of unknown values for the points situated outside the ranges of nodes.
Published in Chapter:
Data Analyzing via Probabilistic Modeling: Interpolation and Extrapolation
Dariusz Jacek Jakóbczak (Koszalin University of Technology, Poland)
Copyright: © 2021 |Pages: 27
DOI: 10.4018/978-1-7998-4706-9.ch002
Abstract
Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.
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More Results
Decision Making and Data Analysis: Curve Modeling via Probabilistic Method
Calculation of unknown values for the points situated outside the ranges of nodes.
Full Text Chapter Download: US $37.50 Add to Cart
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