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Proceedings Paper

A proposed optical system for implementing the novel super-fast image processing scheme: the LPED method
Author(s): Chialun John Hu
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Paper Abstract

LPED method, or Local Polar Edge Detection method, is a novel method the author discovered and implemented in many image processing schemes in the last 3 years with 3 papers published in this and other SPIE national conferences. It uses a special real-time boundary extraction method applied to some binary images taken by an uncooled IR camera on some high temperature objects embedded in a cold environment background in the far field. The unique boundary shape of each high temperature object can then be used to construct a 36D analog vector (a 36 − “digit” number U, with each “digit” being a positive analog number of any magnitude). This 36D analog vector U then represents the ID code to identify this object possessing this particular boundary shape. Therefore, U may be used for tracking and targeting on this particular object when this object is moving very fast in a 2D space and criss-crossing with other fast moving objects embedded in the same field of view. The current paper will report a preliminary optical bench design of the optical system that will use the above developed soft-ware to construct a real-time, instant-detect, instant track, and automatic targeting high power laser gun system, for shooting down any spontaneously launched enemy surface-to-air-missiles from the near-by battle ground. It uses the total reflection phenomenon in the Wollastron beam combiner and real-time monitor screen auto-targeting and firing system to implement this “instant-detect, instant-kill, SAM killer system”.

Paper Details

Date Published: 5 May 2014
PDF: 10 pages
Proc. SPIE 9094, Optical Pattern Recognition XXV, 90940D (5 May 2014); doi: 10.1117/12.2048571
Show Author Affiliations
Chialun John Hu, Southern Illinois Univ. at Carbondale (United States)
SunnyFuture Software (United States)


Published in SPIE Proceedings Vol. 9094:
Optical Pattern Recognition XXV
David Casasent; Tien-Hsin Chao, Editor(s)

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