Minimax eigenvalue transform

Gerhard X. Ritter; Peter Sussner

doi:10.1117/12.60648

1 June 1992 Minimax eigenvalue transform

Gerhard X. Ritter, Peter Sussner

Proceedings Volume 1769, Image Algebra and Morphological Image Processing III; (1992) https://doi.org/10.1117/12.60648
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

The purpose of this paper is to present a radically new image transform, called the Minimax Eigenvector Decomposition (MED) transform. This novel transform is based on the minimax product of two matrices and is an analogue of the Singular Value Decomposition (SVD) transform of linear algebra. In comparison to the SVD transform, in the MED transform eigenvalues need not be computed as they turn out to be zero. Furthermore, computation of eigenvectors is trivial. This makes the use of the MED transform more desirable as the major problem associated with the SVD transform is the computation of the singular values and eigenvectors. These are computationally extensive and often lead to significant numerical errors.

Citation Download Citation

Gerhard X. Ritter and Peter Sussner "Minimax eigenvalue transform", Proc. SPIE 1769, Image Algebra and Morphological Image Processing III, (1 June 1992); https://doi.org/10.1117/12.60648

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