AN EMPIRICAL EVALUATION OF INTEREST POINT DETECTORS
Íñigo Barandiarán, Manuel Graña, Marcos Nieto · Cybernetics & Systems · 2013
Image interest point extraction and matching across images is a commonplace task in computer vision–based applications, across widely diverse domains, such as 3D reconstruction, augmented reality, or tracking. We present an empirical evaluation of state-of-the-art interest point detection algorithms measuring several parameters, such as efficiency, robustness to image domain geometric transformations—that is, similarity—affine or projective transformations, as well as invariance to photometric transformations such as light intensity or image noise.