New automatic multi-level thresholding technique for segmentation of thermal images

Jung Shiong Chang*, Hong Yuan Mark Liao, Maw Kae Hor, Jun-Wei Hsieh, Ming Yang Chern

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


A new wavelet-based automatic multi-level thresholding technique is proposed. The new technique is a generalized version of the method proposed by Olivo [1]. Olivo [1] proposed using a set of dilated wavelets to convolve with the histogram of an image. For each scale, a set of thresholds was determined automatically based on the rules he proposed. However, Olivo did not provide a systematic way to decide on an exact set of thresholds which corresponds to a specific scale that can lead to the best segmentation result. In this paper, we propose using a cost function as a guide to solve the above problem. Experimental results show that our approach can always automatically select the best scale for performance of multi-level thresholding.

Original languageEnglish
Pages (from-to)23-34
Number of pages12
JournalImage and Vision Computing
Issue number1
StatePublished - 1 Jan 1997


  • Image segmentation
  • Multi-level thresholding
  • Wavelets


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