Experimental investigation of a real-time nonlinear joint transform      correlator

Jun Li; Yurong Wang; Jia-sheng Hu

doi:10.1117/12.173569

1 October 1994 Experimental investigation of a real-time nonlinear joint transform correlator

Jun Li, Yurong Wang, Jia-sheng Hu

Author Affiliations +

Optical Engineering, Vol. 33, Issue 10, (October 1994). https://doi.org/10.1117/12.173569

Abstract

A hybrid digital-optical joint transform correlator has been built for recognition of objects with noise and multiple objects. The correlator uses two optically addressed LCLVs as input transducer and for intensity-recording the joint transform spectrum, two optical lenses for performing a Fourier transform, a CCD camera as active output transducer, and a digital system for postprocessing the output signal and refreshing the reference image through our object library. In our experiment, the spatially separated images are imaged onto one light valve, whose output is written on the second light valve. The output of the second light valve is optically processed to produce a correlation signal at the output transducer. The correlator is supported by a digital system so that the output can be digitized for further processing. Some experimental results for multiple-object recognition are presented and discussed.

Citation Download Citation

Jun Li, Yurong Wang, and Jia-sheng Hu "Experimental investigation of a real-time nonlinear joint transform correlator," Optical Engineering 33(10), (1 October 1994). https://doi.org/10.1117/12.173569

Published: 1 October 1994

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