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Abstract
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Article Information:
Adaptive Image Thresholding Based on the Peak Signal-to-noise Ratio
Farshid PirahanSiah, Siti Norul Huda Sheikh Abdullah and Shahnorbanun Sahran
Corresponding Author: Farshid PirahanSiah
Submitted: May 19, 2014
Accepted: July 01, 2014
Published: September 05, 2014 |
Abstract:
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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.
Key words: Image processing, image segmentation, optical character recognition, single thresholding, , ,
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Cite this Reference:
Farshid PirahanSiah, Siti Norul Huda Sheikh Abdullah and Shahnorbanun Sahran, . Adaptive Image Thresholding Based on the Peak Signal-to-noise Ratio. Research Journal of Applied Sciences, Engineering and Technology, (9): 1104-1116.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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