CAJ | 학술논문

在机器视觉和模式识别的研究中，将图像变换为二值图像是能够更高效识别图像中的特定区域或者目标的关键。提出了一种基于k中心点聚类算法的图像二值化方法（image binarization k-medoids-based clus-tering，IBkMC）。该方法使用基于距离的平方和误差作为聚类质量度量，根据图像二值化的领域知识将k的值取为2，自然地将图像分为前景类和背景类两类。实验结果证明，针对复杂环境下的自然图像，该方法在效果和效率上优于OSTU（最大类间方差）阈值化方法。
재궤기시각화모식식별적연구중，장도상변환위이치도상시능구경고효식별도상중적특정구역혹자목표적관건。제출료일충기우k중심점취류산법적도상이치화방법（image binarization k-medoids-based clus-tering，IBkMC）。해방법사용기우거리적평방화오차작위취류질량도량，근거도상이치화적영역지식장k적치취위2，자연지장도상분위전경류화배경류량류。실험결과증명，침대복잡배경하적자연도상，해방법재효과화효솔상우우OSTU（최대류간방차）역치화방법。
In the research on machine vision and pattern recognition, transforming the image into two-value image is the key foundation to more efficiently recognize specific area or target of image. This paper presents an image binarization processing method using k-medoids clustering. This method uses square sum error based on distance as the clustering quality metric function. According to the field knowledge of image binarization, this method sets the value of k as 2, divides the image into foreground class and background class. The experimental results show that, for natural images under complex environment, this method is better than OSTU thresholding method in the effec-tiveness and efficiency.