摘要
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 clustering,IBk MC)。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。
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 effectiveness and efficiency.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第2期234-241,共8页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金~~
关键词
图像二值化
k中心点聚类
阈值
image binarization
k-medoids clustering
threshold
