COMPARISON OF ROBUST COLOR RECOGNITION ALGORITHMS
Samuel Kosolapov
Страници: 274-283
Публикувана: 23 Sep 2021
Преглеждания: 1,871
Изтегляния: 206
Citations: 2 (Google Scholar)
Резюме: A number of color recognition algorithms are known - from using classic HSL space to modern AI algorithms. However, when analyzing real-life industrial, agricultura,l or medical images (for example images of fruits, flowers – to be sorted by color, human skin – to be analyzed for the presence of suspicious colors) - camera noise makes color recognition non-reliable, especially in dark regions. In this paper reliability of a number of well-known color recognition algorithms was compared. In order to lessen the amount of false recognitions, some modifications to algorithms in test were added. Provided comparisons of the algorithms in test were executed by using real-life images.
Ключови думи: image processing, color recognition, hue, scalar product, normalized correlation
Цитирайте тази статия: Samuel Kosolapov. COMPARISON OF ROBUST COLOR RECOGNITION ALGORITHMS. Journal of International Scientific Publications: Materials, Methods & Technologies 15, 274-283 (2021). https://www.scientific-publications.net/en/article/1002218/
Обратно към съдържанието на тома
© 2026 The Author(s). This is an open access article distributed under the terms of the
Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.
Disclaimer: The Publisher and/or the editor(s) are not responsible for the statements, opinions, and data contained in any published works. These are solely the views of the individual author(s) and contributor(s). The Publisher and/or the editor(s) disclaim any liability for injury to individuals or property arising from the ideas, methods, instructions, or products mentioned in the content.
