A comparative study of image feature detection and description methods for robot vision

Martin Gonzalez-Ruiz; Victor H. Diaz-Ramirez; Rigoberto Juarez-Salazar

doi:10.1117/12.2528581

6 September 2019 A comparative study of image feature detection and description methods for robot vision

Martin Gonzalez-Ruiz, Victor H. Diaz-Ramirez, Rigoberto Juarez-Salazar

Proceedings Volume 11136, Optics and Photonics for Information Processing XIII; 111360P (2019) https://doi.org/10.1117/12.2528581
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Detection and description of local features in images is an essential task in robot vision. This task allows to identify and uniquely specify stable and invariant regions in a observed scene. Many successful detectors and descriptors have been proposed. However, the proper combination of a detector and a descriptor is not trivial because there is a trade-off among different performance criteria. This work presents a comparative study of successful image feature detection and description methods in the context of the simultaneous localization and mapping problem. The considered methods are exhaustively evaluated in terms of accuracy, robustness, and processing time.

Conference Presentation

Citation Download Citation

Martin Gonzalez-Ruiz, Victor H. Diaz-Ramirez, and Rigoberto Juarez-Salazar "A comparative study of image feature detection and description methods for robot vision", Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360P (6 September 2019); https://doi.org/10.1117/12.2528581

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available