An Empirical Evaluation of Interest Point Detectors

Authors: Iñigo Barandiaran, Manuel Graña, Marcos Nieto

Date: 01.03.2013

Cybernetics and Systems


PDF

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

@Article {
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},
}
Vicomtech

Parque Científico y Tecnológico de Gipuzkoa,
Paseo Mikeletegi 57,
20009 Donostia / San Sebastián (Spain)

+(34) 943 309 230

Zorrotzaurreko Erribera 2, Deusto,
48014 Bilbao (Spain)

close overlay

Behavioral advertising cookies are necessary to load this content

Accept behavioral advertising cookies