Recognizing objects in 3D data with distinctive self-similarity features

Suya You; Jing Huang

doi:10.1117/12.2305110

30 April 2018 Recognizing objects in 3D data with distinctive self-similarity features

Suya You, Jing Huang

Proceedings Volume 10648, Automatic Target Recognition XXVIII; 106480D (2018) https://doi.org/10.1117/12.2305110
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Local features with invariant descriptions are important for many tasks in image processing and computer vision. This paper presents a new local feature descriptor for 3D object and scene representation. The new descriptor, named 3DSSIM, explores the internal geometric property of layout similarity of 3D objects to produce efficient feature representation. The 3D-SSIM is highly distinctive, quick to compute, and shows superior advantages in terms of robustness to noises, invariance to viewpoints, and tolerance to geometric distortions. We extensively evaluated performance of the new descriptor with various datasets.

Conference Presentation

Citation Download Citation

Suya You and Jing Huang "Recognizing objects in 3D data with distinctive self-similarity features", Proc. SPIE 10648, Automatic Target Recognition XXVIII, 106480D (30 April 2018); https://doi.org/10.1117/12.2305110

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