Abstract
This study aims to define new simple shape descriptors to analyze airfoils. The ImageJ platform is used to calculate twelve different shape descriptors such as area, convex hull, contour temperature and solidity by performing image processing. One of the most important findings is that an increase in the thickness of an airfoil leads to corresponding increases in its area, perimeter, area of minimum enclosing area, and convex hull area. Another noteworthy discovery is that the values derived from these basic features, either increasing or decreasing. Simple shape features in the study are not used independently, as they do not possess distinct characteristics that set them apart from one another. Machine learning and deep learning applications can achieve greater success when these features are combined with other elements. The combination of these features with other shape attributes, such as chain code histograms, shape signatures, and central moments, can enhance the success of machine learning and deep learning applications.
Published Version
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