摘要
自适应二值化技术广泛应用于图像分割和目标边缘的提取,其阈值的确定是数字图像处理的关键技术。经典Otsu算法是穷举式的阈值确定方法,存在较大的计算冗余。在内存和资源都十分有限的条件下,提出了一种基于图像复杂度的一维Otsu改进算法,根据图像复杂程度的不同,该算法在满足准确度要求的基础上提高了Otsu算法的速度。在DM3730实验板上进行了实验,结果表明,该算法的复杂度低于经典算法,计算速度可提升40%左右,可以满足嵌入式系统的实时性要求,且分割效果与原始算法基本一致。
Self-adaptive binaryzation is widely used in image segmentation and edge detection.Threshold extraction is one of the key technologies of digital image processing.Classic Otsu algorithm is an exhaustivity way and there will be a large computing redundancy.On condition of limited computer RAM and resource,this paper put forward a modified one-dimensional Otsu algorithm based on image complexity.According to different complexity,it improves Otsu algorithm speed on the basis of accuracy requirements.According to the experiments done on DM3730,this algorithm's complexity is better than the classic one and its speed can be improved by about 40%.It can meet embedded system's real-time requirements and the segmentation effect is almost the same with the classic one.
出处
《计算机科学》
CSCD
北大核心
2015年第S1期171-174,共4页
Computer Science
关键词
大津法
图像复杂度
平均灰度
自适应分割
Otsu algorithm,Image complexity,Average gray value,Self-adaptive segmentation
