• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.29-37
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• 2006

Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.126-128
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• 2006

For pattern matching, an object image should be segmented and analyzed for the first time. Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding is one of the most veil-known methods proposed in the literature. However, the method has a disadvantage of repeatedly searching the optimal thresholds for the entire region. To overcome this problem, a number of methods have been proposed. In this paper, we propose a simple and fast thresholding method of finding multi-level threshold values by extending the Otsu's method. Our experimental results for the benchmak images show a possibility that the proposed method could be used efficiently for pattern matching.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.216-224
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• 2013

The within-class variance of Otsu's method is moderate but improper in expressing class statistical distributions. Otsu's method uses a variance to represent the distribution of each class. The variance utilizes a distance square from the mean to a data. This process is not proper in denoting a real class statistical distribution because of the distance square. In this paper, to express more exact class statistical distributions, the within-class standard deviation as a criterion for threshold selection is proposed and then the optimal threshold is determined by minimizing it. In order to have validity, it is shown through the experimental results that the proposed method was more superior to the counterparts.