CAJ | 학술논문

分析Otsu阈值性质可以为应用和改进Otsu方法提供理论依据.证明了Otsu方法找出的最佳阈值是用该阈值分割出的两类的均值的平均值.当两类的类内方差差别较大时,Otsu方法的分割阈值将偏向类内方差大的一类,从而将类内方差大的一类的部分像素划分到类内方差小的类中.针对这种情况,提出了一种约束灰度范围的改进型Otsu方法.实验结果表明,新算法的性能明显优于传统的Otsu方法.
분석Otsu역치성질가이위응용화개진Otsu방법제공이론의거.증명료Otsu방법조출적최가역치시용해역치분할출적량류적균치적평균치.당량류적류내방차차별교대시,Otsu방법적분할역치장편향류내방차대적일류,종이장류내방차대적일류적부분상소화분도류내방차소적류중.침대저충정황,제출료일충약속회도범위적개진형Otsu방법.실험결과표명,신산법적성능명현우우전통적Otsu방법.
The analysis of the properties of the threshold in the Otsu segmentation method can provide theoretical help for improving and applying the Otsu method. This paper proves a conclusion that the threshold of the Otsu method is the average of the means of two classes partitioned by this threshold. Thus, when the difference of the two within-class variances is large, the threshold of the Otsu method tends to be closer to the class with larger within-class variance, which means that more pixels of this class will be classified into the another class. To overcome this problem, this paper presents an improved Otsu algorithm by constraining the search range of the ideal segmentation threshold. Experimental results show the superiority of the proposed algorithm by yielding more reasonable segmentation results compared to the traditional Otsu method.