CAJ | 학술논문

图像分割是图像分析的关键步骤,其中阈值分割方法是最简单也是应用最广泛的方案. Otsu方法在应用于通用的现实图片时,由于其保持着良好的稳定性和分割目标的形状测度,被认为是最好的方法之一. 但是大量研究表明对于2类方差差异很大的图像,其阈值严重偏离最优阈值,而偏向方差大的一类. 研究了Otsu最优准则和现有改进算法的特性,进而基于前景与背景方差差异先验信息提出了新的最优化准则. 与现存的非类间方差阈值法和对Otsu阈值法进行改进的方法进行比较表明,该方法具有最优的特性,同时不需要可变参数.
도상분할시도상분석적관건보취,기중역치분할방법시최간단야시응용최엄범적방안. Otsu방법재응용우통용적현실도편시,유우기보지착량호적은정성화분할목표적형상측도,피인위시최호적방법지일. 단시대량연구표명대우2류방차차이흔대적도상,기역치엄중편리최우역치,이편향방차대적일류. 연구료Otsu최우준칙화현유개진산법적특성,진이기우전경여배경방차차이선험신식제출료신적최우화준칙. 여현존적비류간방차역치법화대Otsu역치법진행개진적방법진행비교표명,해방법구유최우적특성,동시불수요가변삼수.
Image segmentation is a fundamental step in image processing, and threshold segmentation is the simplest and most widely used method among the segmentation methods. The classic Otsu method is deemed as one of the best methods for general real world images with regard to uniformity and shape measure. However, a lot of research shows that, for two classes of image with large variance difference, the threshold seriously deviates from the opti-mum threshold and inclines to the type with larger variance. In this paper, optimal Otsu criteria and the properties of an existing improved version are analyzed, then a novel criterion of optimization is proposed by combining prior knowledge about the variance discrepancy between background and foreground. The method is compared with the current non-between-class variance threshold methods and some improved Otsu threshold methods. The results show that our method is optimal, with no need for variable parameters.