By Topic

Markov random field regularisation models for adaptive binarisation of nonuniform images

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
$33 $33
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

2 Author(s)
D. Shen ; Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong ; H. H. S. Ip

Two related MRF models, an edge-preserving smoothing model followed by a modified standard regularisation, are presented for the adaptive binarisation of nonuniform images in the presence of noise. In particular, a computational model is developed for a modified standard regularisation method which calculates the adaptive threshold surface for noisy images. Since the modified standard regularisation depends only on the image data, and not its edge segments, it gives much better performance and can be applied to more classes of image than those methods that solely rely on edge segments. Experimental results demonstrate that the proposed method has the best performance over three other commonly used adaptive segmentation methods and is faster than previous interpolation-based thresholding techniques

Published in:

IEE Proceedings - Vision, Image and Signal Processing  (Volume:145 ,  Issue: 5 )