Abstract: Corner detection is a crucial image processing technique that has a wide range of application, including motion detection, image registration, video tracking, and object recogni-tion. Most proposed approaches for corner detection are based on gray-scale images, despite it has been shown that color infor-mation can greatly improve the quality of corners detection. This paper aims to introduce a new operator that identifies the second-order image information for multi-spectral images. The operator is developed using the multi-spectral gradient and differential structures of the image. Consequently, the eigenvectors of the proposed operator are used for detecting corners. A comparative study is conducted using synthetic and real images, and the result confirms that the proposed approach performs better compared with two other approaches for detecting corners.

Hassan El Houari and Ahmed Fouad El Ouafdi, “A New Corner Detection Operator for Multi-Spectral Images” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120491

@article{Houari2021,
title = {A New Corner Detection Operator for Multi-Spectral Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120491},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120491},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Hassan El Houari and Ahmed Fouad El Ouafdi}
}