@inproceedings{f6a3807357014a57a9bc9e82d7f2bbd2,
title = "Keypoint detection based on the unimodality test of HOGs",
abstract = "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.",
keywords = "HOG, keypoint detection, repeatability, salient feature, unimodality test",
author = "Cata{\~n}o, {M. A.} and J. Climent",
year = "2012",
doi = "10.1007/978-3-642-33179-4_19",
language = "English",
isbn = "9783642331787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "189--198",
booktitle = "Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers",
edition = "PART 1",
note = "8th International Symposium on Visual Computing, ISVC 2012 ; Conference date: 16-07-2012 Through 18-07-2012",
}