Distortion-invariant optical pattern recognition without correlation

Michael E. Lhamon; Laurence G. Hassebrook; Raymond C. Daley

doi:10.1117/12.205785

28 March 1995 Distortion-invariant optical pattern recognition without correlation

Michael E. Lhamon, Laurence G. Hassebrook, Raymond C. Daley

Proceedings Volume 2490, Optical Pattern Recognition VI; (1995) https://doi.org/10.1117/12.205785
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Most distortion-invariant optical pattern recognition techniques rely on correlation which inherently achieves translation-invariance. We introduce a new formulation for image recognition where only 'vector inner product' (assuming 2D images are lexicographically converted to vectors) operations are used to achieve distortion-invariant pattern recognition. Our formulation expands the linear phase coefficient composite filter family, developed by Hassebrook et.al., into a set of translation- and distortion-invariant vector inner product operators. The set of vector inner products are optimal in numerical efficiency because they represent a Karhunen Loeve expansion. Translation- invariance is achieved by embedding 2D translation into the training set as two additional distortion parameters. The magnitude of the vector inner product results in a response insensitive to translation and distortion, where as the phase response varies, but is discarded. For large images containing many objects this method can be applied by tiling the vector inner product operators to the test image size. Examples of our approaches, distortion-invariant detection/discrimination capabilities, numerical efficiency, and tradeoffs between conventional correlation are presented.

Citation Download Citation

Michael E. Lhamon, Laurence G. Hassebrook, and Raymond C. Daley "Distortion-invariant optical pattern recognition without correlation", Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); https://doi.org/10.1117/12.205785

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