Markov random field regularisation models for adaptive binarisation of nonuniform images

D. Shen, H. H.S. Ip

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)322-332
Number of pages11
JournalIEE Proceedings: Vision, Image and Signal Processing
Volume145
Issue number5
DOIs
Publication statusPublished - 1998
Externally publishedYes

Keywords

  • Adaptive image binarisation
  • Edge-preserving smoothing
  • Markov random field models
  • Standard regularisation
  • Threshold surface

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Markov random field regularisation models for adaptive binarisation of nonuniform images'. Together they form a unique fingerprint.

  • Cite this