An Empirical Evaluation of Interest Point Detectors
Abstract
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.
BIB_text
author = {Iñigo Barandiaran, Manuel Graña, Marcos Nieto},
title = {An Empirical Evaluation of Interest Point Detectors},
journal = {Cybernetics and Systems},
pages = {98-117},
volume = {44},
keywds = {
computer vision, feature descriptors, interest points, point matching
}
abstract = {
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.
}
isi = {1},
date = {2013-03-01},
year = {2013},
}
