A Computer-Aided Design (CAD) Embroidery Font Digitizing System

Andreadis, Georgios; Lampridou, Efstratia; Sherar, Peter A.

doi:10.12720/joace.3.1.15-22

Cited by 2 publications

(1 citation statement)

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“…However, estimating the function of a curve based on several points is challenging. As the shape of the curve becomes more complex, more data is required to represent it accurately [21]. When the available data is insufficient, breaking the curve into stepwise classifications is the best way to reduce its complexity.…”

Section: A Strategy For Efficiently Estimating the Absolute Boltzmann Entropymentioning

confidence: 99%

A Head/Tail Breaks-Based Method for Efficiently Estimating the Absolute Boltzmann Entropy of Numerical Raster Data

Zhang

2020

IJGI

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Shannon entropy is the most popular method for quantifying information in a system. However, Shannon entropy is considered incapable of quantifying spatial data, such as raster data, hence it has not been applied to such datasets. Recently, a method for calculating the Boltzmann entropy of numerical raster data was proposed, but it is not efficient as it involves a series of numerical processes. We aimed to improve the computational efficiency of this method by borrowing the idea of head and tail breaks. This paper relaxed the condition of head and tail breaks and classified data with a heavy-tailed distribution. The average of the data values in a given class was regarded as its representative value, and this was substituted into a linear function to obtain the full expression of the relationship between classification level and Boltzmann entropy. The function was used to estimate the absolute Boltzmann entropy of the data. Our experimental results show that the proposed method is both practical and efficient; computation time was reduced to about 1% of the original method when dealing with eight 600 × 600 pixel digital elevation models.

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Section: A Strategy For Efficiently Estimating the Absolute Boltzmann Entropymentioning

confidence: 99%