Curvilinear thresholding method for noisy images based on 2D histogram

Jun Zhang, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Image thresholding is a useful method in many image processing and computer vision applications. Otsu method is one of popular thresholding methods and has been frequently used as a classical technique in various applications. A premise of this method is that the probability of edge region is supposed to be zero. However, the premise is not proper in some situations such as the images are corrupted by noises. To solve this problem, a curvilinear thresholding method (CTM) is proposed based on the traditional Otsu method. We give a recursive algorithm of line thresholding method (LTM), which is a particular case of CTM, to verify our ideas. In addition, Otsu based method has another weakness that is it gives satisfactory results only when the numbers of pixels in each class are close to each other. Otherwise the threshold will be biased. This paper introduces a two-dimensional (2D) histogram projection method to correct the Otsu threshold. A fast algorithm for searching the valley of one-dimensional (1D) projected histogram is also given based on wavelet transform. Experimental results show that the proposed method performs much better than the traditional Otsu method.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008
PublisherIEEE Computer Society
Pages1014-1019
Number of pages6
ISBN (Print)9781424426799
DOIs
Publication statusPublished - 2009
Event2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008 - Bangkok, Thailand
Duration: 2009 Feb 212009 Feb 26

Publication series

Name2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008

Conference

Conference2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008
CountryThailand
CityBangkok
Period09/2/2109/2/26

Keywords

  • 2D histogram projection
  • Curvilinear thresholding method (CTM)
  • Line thresholding method (LTM)
  • Noisy image

ASJC Scopus subject areas

  • Biotechnology
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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