Multiple-target detection by using joint transform correlator with compressed reference images

Widjaja, Joewono; Suripon, Ubon

doi:10.1016/j.optcom.2005.04.042

Cited by 11 publications

(1 citation statement)

References 5 publications

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“…In this regard, in the past decades, optical correlation technique has been proven to be an excellent potential scheme for targets detection and tracking applications as well as others optical information processing applications, such as in optical cryptosystems 6 – 10 In particular, the optical joint transform correlation (JTC) architecture makes pattern recognition closer to near real-time processor over the classical optical VanderLugt correlation, 11 , 12 which suffers from poor light efficiency, large correlation sidelobes, and large autocorrelation width, in addition to the need of precise alignment of the optical elements and the fabrication of complex-valued filters. Over time, developers show more interests in real-time JTC-based processors due to fast development of electrically addressed spatial light modulator (SLM).…”

Section: Introductionmentioning

confidence: 99%

Class-associative multiple target recognition for highly compressed color images in a joint transform correlator

Cherri

Nazar

2022

Opt. Eng.

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We propose a practical near real-time compression-based class-associative multiple target detection technique for color images. The size of the color images is reduced to its utmost ratio to speed up the processing time and to increase the storage capacity of the recognition system. A fringe-adjusted joint transform correlation technique is employed to successfully detect the compression-based multiple targets in colored images. In addition, to eliminate the false alarms and zero-order terms due to multiple desired and undesired objects in an input scene, we have used the shifted phase-encoded and the reference phase-encoded techniques. The performance of detecting the class-associative multiple targets for large compression ratios (up to 94%) and under strong noisy conditions (Gaussian and salt and pepper noises) is assessed through many computer simulations experiments. Furthermore, detecting small targets or even a small part of a target is presented for images occluded up to 90%.

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