Keypoint detection based on the unimodality test of HOGs

M. A. Cataño, J. Climent

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


We present a new method for keypoint detection. The main drawback of existing methods is their lack of robustness to image distortions. Small variations of the image lead to big differences in keypoint localizations. The present work shows a way of determining singular points in an image using histograms of oriented gradients (HOGs). Although HOGs are commonly used as keypoint descriptors, they have not been used in the detection stage before. We show that the unimodality of HOGs can be used as a measure of significance of the interest points. We show that keypoints detected using HOGs present higher robustness to image distortions, and we compare the results with existing methods, using the repeatability criterion.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers
Number of pages10
EditionPART 1
StatePublished - 2012
Event8th International Symposium on Visual Computing, ISVC 2012 - Rethymnon, Crete, Greece
Duration: 16 Jul 201218 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Symposium on Visual Computing, ISVC 2012
CityRethymnon, Crete


  • HOG
  • keypoint detection
  • repeatability
  • salient feature
  • unimodality test


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