Curve Enhancement Using Rule-Based Relaxation

Philipp W. Besslich; Henning Bassmann

doi:10.1117/12.950275

2 March 1989 Curve Enhancement Using Rule-Based Relaxation

Philipp W. Besslich, Henning Bassmann

Proceedings Volume 1027, Image Processing II; (1989) https://doi.org/10.1117/12.950275
Event: 1988 International Congress on Optical Science and Engineering, 1988, Hamburg, Germany

Abstract

A new approach to curve enhancement in gradient images is presented. Its basic idea is a rule-based type of the relaxation process. In order to create rules we define a set of attributes, each of which has an associated set of attribute values. These are used to create the condition parts of the rules. Attributes and their values describe typical constellations of a curve in the vicinity of the central pixel of a window. These constellations are initially generated by a gradient operator followed by a nonmaxima absorption operation. If the actual constellation of a curve in a window matches one of the rules, its action part is activated. This part of the rule concerns the updating of the central pixel. The updating process follows the well-known relaxation scheme: If the constellation indicates a "good" curve the gradient magnitude of the central pixel is to be "reinforced". However, if the constellation is "fuzzy" the magnitude is "weakend". Using an induction algorithm the rule base is transformed into a compact decision tree that controls the relaxation process according to the current constellation. The method was found to perform very fast compared to other algorithms and to roduce thin curves representing the local contrast. Moreover, the method is highly flexible: changing of parameters and/or rules make modifications easy to implement.

Citation Download Citation

Philipp W. Besslich and Henning Bassmann "Curve Enhancement Using Rule-Based Relaxation", Proc. SPIE 1027, Image Processing II, (2 March 1989); https://doi.org/10.1117/12.950275

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