"Find efficient method for detecting features in digital images"

Abstract

Many computer vision applications, such as pattern recognition, 3D modelling, and image stitching, rely heavily on feature detection and matching. Furthermore, in recent decades, this has been a remarkably busy area of research, with multiple publications published in this field. However, there are still several obstacles to overcome in this field of study. As a result, researchers are encouraged to develop new approaches to meet these needs and resolve existing problems. (O’Mahony et al., 2019) Local and global features are the two sorts of features. Local features are parts of a picture, such as a pattern, edge, or point, that differ in texture or colour from its surroundings (Murty and Devi 2015). Features that characterise the entire image are referred to as global features. Local features are more robust than global features, thus most studies focus on them (Murty and Devi 2015). So, the main goal of our work is to establish new software able to work with the most universal image extension and can be