Adaptive image thresholding based on the peak signal-to-noise Ratio

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Abstract

The aim of this research is to enhance a Peak signal Noise Ratio based thresholding algorithm. Thresholding is a critical step in pattern recognition and has a significant effect on the subsequent steps in imaging applications. Thresholding is used to separate objects from the background and decreases the amount of data and increases the computational speed. Recently, there has been an increased interest in multilevel thresholding. However, as the number of levels increases, the computation time increases. In addition, single threshold methods are faster than multilevel methods. Moreover, for each new application, new methods must be developed. In this study, a new algorithm that applies the peak signal-to-noise ratio method as an indicator to segment the image is proposed. The algorithm was tested using the license plate recognition system, DIBCO, 2009 and standard images. The proposed algorithm is comparable to existing methods when applied to Malaysian vehicle images. The proposed method performs better than earlier methods, such as Kittler and Illingworth's Minimum Error Thresholding, potential difference and Otsu. In general, the proposed algorithm yields better results for standard images. In the license plate recognition application, the new method yielded an average performance.

Original languageEnglish
Pages (from-to)1104-1116
Number of pages13
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume8
Issue number9
Publication statusPublished - 2014

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Signal to noise ratio
Pattern recognition
Imaging techniques

Keywords

  • Image processing
  • Image segmentation
  • Optical character recognition
  • Single thresholding

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Cite this

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title = "Adaptive image thresholding based on the peak signal-to-noise Ratio",
abstract = "The aim of this research is to enhance a Peak signal Noise Ratio based thresholding algorithm. Thresholding is a critical step in pattern recognition and has a significant effect on the subsequent steps in imaging applications. Thresholding is used to separate objects from the background and decreases the amount of data and increases the computational speed. Recently, there has been an increased interest in multilevel thresholding. However, as the number of levels increases, the computation time increases. In addition, single threshold methods are faster than multilevel methods. Moreover, for each new application, new methods must be developed. In this study, a new algorithm that applies the peak signal-to-noise ratio method as an indicator to segment the image is proposed. The algorithm was tested using the license plate recognition system, DIBCO, 2009 and standard images. The proposed algorithm is comparable to existing methods when applied to Malaysian vehicle images. The proposed method performs better than earlier methods, such as Kittler and Illingworth's Minimum Error Thresholding, potential difference and Otsu. In general, the proposed algorithm yields better results for standard images. In the license plate recognition application, the new method yielded an average performance.",
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