Combining blur and affine moment invariants in object recognition

Yingchun Li; Hexin Chen; Jiujun Zhang; Pengfei Qu

doi:10.1117/12.521525

2 September 2003 Combining blur and affine moment invariants in object recognition

Yingchun Li, Hexin Chen, Jiujun Zhang, Pengfei Qu

Proceedings Volume 5253, Fifth International Symposium on Instrumentation and Control Technology; (2003) https://doi.org/10.1117/12.521525
Event: Fifth International Symposium on Instrumentation and Control Technology, 2003, Beijing, China

Abstract

In many application areas such as military photointerpretation and remote sensing, images are usually subjected to geometric distortion and blur degradation. The determination of invariant characteristics is an important problem in pattern recognition. In this paper, an approach to derive blur and affine combined invariants is presented to recognize the objects. As we prove in the paper, they can be constructed by combining affine moment invariants and blur invariants derived earlier. The tests show that combined invariants can recognize objects in the degraded image without any restoration and geometric normalization.

Citation Download Citation

Yingchun Li, Hexin Chen, Jiujun Zhang, and Pengfei Qu "Combining blur and affine moment invariants in object recognition", Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); https://doi.org/10.1117/12.521525

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