Keypoint detection based on the unimodality test of HOGs

M. A. Cataño; J. Climent

doi:10.1007/978-3-642-33179-4_19

Keypoint detection based on the unimodality test of HOGs

M. A. Cataño, J. Climent

Polytechnic University of Catalonia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers
Pages	189-198
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-33179-4_19
State	Published - 2012
Event	8th International Symposium on Visual Computing, ISVC 2012 - Rethymnon, Crete, Greece Duration: 16 Jul 2012 → 18 Jul 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7431 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Visual Computing, ISVC 2012
Country/Territory	Greece
City	Rethymnon, Crete
Period	16/07/12 → 18/07/12

Keywords

HOG
keypoint detection
repeatability
salient feature
unimodality test

Access to Document

10.1007/978-3-642-33179-4_19

Cite this

Cataño, M. A., & Climent, J. (2012). Keypoint detection based on the unimodality test of HOGs. In Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers (PART 1 ed., pp. 189-198). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7431 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-33179-4_19

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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.",

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Cataño, MA & Climent, J 2012, Keypoint detection based on the unimodality test of HOGs. in Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7431 LNCS, pp. 189-198, 8th International Symposium on Visual Computing, ISVC 2012, Rethymnon, Crete, Greece, 16/07/12. https://doi.org/10.1007/978-3-642-33179-4_19

Keypoint detection based on the unimodality test of HOGs. / Cataño, M. A.; Climent, J.
Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers. PART 1. ed. 2012. p. 189-198 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7431 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Cataño MA, Climent J. Keypoint detection based on the unimodality test of HOGs. In Advances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers. PART 1 ed. 2012. p. 189-198. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-33179-4_19

Keypoint detection based on the unimodality test of HOGs

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