The calculation of the minima and maxima from convex hulls is an important research area in optimization, machine learning, and related fields. Mathematics and machine learning researchers have come up with innovative algorithms to quickly calculate the convex hulls to improve the efficiency of these algorithms.

The paper by Asaeedi et al. introduces an alpha-convex hull calculation method, which uses polygons and angles to find the shape of the hull. The math discussed in the paper is easy to follow and is able to create theorems and justify them, explaining how polygons and angles can be used to engulf patterns in images for sectioning off certain parts of the image.

In the paper, the authors compare the techniques to other similar approaches, mostly mathematical approaches, of calculating areas of interest in images and pattern recognition problems.

Being a computer scientist, I find it easy to follow and understand the logic being discussed. However, I fail to see how it can be applied to larger computing problems. The paper does not discuss how the technique can be scaled and how it can be used as a part of programming code. As the paper is published in Theoretical Computer Science, writing an algorithm may have been key to seeing how it could be used in programming code. For now, I would have to think hard to see how this could be implemented.