A New Corner Detection Operator for Multi-Spectral Images

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 recognition. Most proposed approaches for corner detection are based on gray-scale images, despite it has been shown that color information 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.