Demonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications

Kenia Picos; Ulises Orozco-Rosas; Victor Diaz-Ramirez

doi:10.1117/12.2528944

6 September 2019 Demonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications

Kenia Picos, Ulises Orozco-Rosas, Victor Diaz-Ramirez

Author Affiliations +

Proceedings Volume 11136, Optics and Photonics for Information Processing XIII; 111360O (2019) https://doi.org/10.1117/12.2528944
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

This paper proposes frequency-domain correlation filtering to solve object recognition of three-dimensional (3D) targets. We perform a linear correlation in the frequency domain between an input frame of the video sequence and a designed filter. This operation measures the correspondence between the two signals. In order to produce a high matching score, we design a bank of correlation filters, in which each filter contains unique information of the target in a single view and statistical parameters of the scene. In this paper, we demonstrate the feasibility of correlation filters used to solve 3D object recognition and their robustness to different image conditions such as noise, cluttered background, and geometrical distortions of the target. The evaluation performance presents a high accuracy in terms of quantitative metrics.

Citation Download Citation

Kenia Picos, Ulises Orozco-Rosas, and Victor Diaz-Ramirez "Demonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications", Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360O (6 September 2019); https://doi.org/10.1117/12.2528944

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