By Topic

A connectionist approach for thresholding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chang, C.-C. ; Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Chang, C.-H. ; Hwang, S.-Y.

Thresholding is a necessary and useful step in many applications of image processing. The general process of thresholding is first to select several gray levels, or thresholds, then use these values to classify the pixels into several subranges. Previous methods for selecting thresholds are usually designed based on assumed distributions of pixels or some sort of heuristics. It is difficult to apply any of these methods when the domain of images is changed. There is a need for seeking a more flexible and robust technique in such situation. The paper presents a connectionist approach for learning and selecting thresholds by using the Kohonen algorithm which is an unsupervised neural network. The approach is able to find thresholds for classifying images without a teacher. Experimental results show that the approach is promising

Published in:

Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on

Date of Conference:

30 Aug-3 Sep 1992