A thresholding method using the mixture of normal density functions
Iwao Sekita, Takio Kurita, N. Otsu, Nabih N. Abdelmalek · 2002
Thresholding techniques are fundamental for image segmentation. It is often realistic to assume that each pixel is subject to the mixture of several normal distributions. The paper proposes a criterion of thresholding a histogram of gray level intensity. It uses a new variance of the histogram. An algorithm which considers the tails of probability density functions of the other classes is also shown. The proposed method is experimentally compared with the Kittler-Illingworth method and the Otsu method. The proposed criterion and the Otsu one are effective in thresholding of handwritten characters. More accurate thresholds are obtained by the algorithm when the data comes from the mixture normal distribution, although the number of computations is increased.>