Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies

David Casasent

doi:10.1117/12.936466

28 November 1983 Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies

David Casasent

Proceedings Volume 0431, Real-Time Signal Processing VI; (1983) https://doi.org/10.1117/12.936466
Event: 27th Annual Technical Symposium, 1983, San Diego, United States

Abstract

Many linear algebra operations, matrix inversions, etc. are required in pattern recognition as well as in signal processing. In this paper, we concentrate on feature extraction pattern recognition techniques (specifically a chord distribution and a moment feature space). For these two case studies, we note the various linear algebra operations required in distortion-invariant pattern recognition. Systolic processors can easily perform all reauired linear algebra functions.

Citation Download Citation

David Casasent "Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies", Proc. SPIE 0431, Real-Time Signal Processing VI, (28 November 1983); https://doi.org/10.1117/12.936466

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