Multispectral edge detection using the two-dimensional self-organizing map

Pekka J. Toivanen; Jarkko Ansamaki; S. Leppajarvi; Jussi P. S. Parkkinen

doi:10.1117/12.341110

9 March 1999 Multispectral edge detection using the two-dimensional self-organizing map

Pekka J. Toivanen, Jarkko Ansamaki, S. Leppajarvi, Jussi P. S. Parkkinen

Proceedings Volume 3647, Applications of Artificial Neural Networks in Image Processing IV; (1999) https://doi.org/10.1117/12.341110
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

Abstract

In this paper ,a new method for edge detection in multispectral imags is presented. It is based on the use of the Self-Organizing Map (SOM), Peano scan and a conventional edge detector. The method presented in this paper order the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium have scalar ordering values near each other. This is achieved using a 2D self-organizing map and the Peano scan. After ordering, the original vector image reduces to a gray-value image, and a conventional edge detector can be applied. In this paper, the Laplace and the Canny edge detectors are used. It is shown, that using the proposed method sit is possible to find the same relevant edges that R-ordering based methods find. Furthermore, it is also possible to find edges in images which consist of metameric colors, i.e. images in which every pixel vector maps into the same location in RGB space. This is not possible using conventional edge detectors which use an RGB image as input. Finally, the new method is tested with a real-world airplane image, giving results comparable with R-ordering based methods.

Citation Download Citation

Pekka J. Toivanen, Jarkko Ansamaki, S. Leppajarvi, and Jussi P. S. Parkkinen "Multispectral edge detection using the two-dimensional self-organizing map", Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); https://doi.org/10.1117/12.341110

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