摘要
为了增强图像的可读性,解决因光照、噪声等因素造成的图像模糊、边缘不清等问题,在前人研究的基础上提出了对Canny算子的改进。用遗传算法对基于GCV准则的阈值函数自动寻优,在降低噪声的同时增强了图像的可读性;用评价函数对Otsu法自适应设定高低阈值,无需考虑阈值比例,减少了伪边缘现象。不同图像的边缘提取实验和数据分析表明,该方法在检测精度、抗噪能力和运算效率等方面都得到明显提高。对于存在的不足提出了进一步研究方向。
In order to enhance the quality of the image, on the basis of previous studies, we improved the Canny operator to solve the problem caused by factors such as light, noise caused by image blur, unclear edge. Firstly, by optimizing the G-CV criteria based threshold function with genetic algorithm automatically, we reduced the image noise and enhanced the quality of the image simultaneously. Secondly, using adaptive evaluation function to set the thresholds for the Otsu, we reduced the phenomenon of false edges regardless to the ratio of the threshold, Different image edge extraction ex- periments and data analysis show that the method is improved in terms of detection accuracy, noise immunity, and opera- tional efficiency significantly. At last, for the shortcomings, we pointed out further research direction.
出处
《计算机科学》
CSCD
北大核心
2013年第6期279-282,共4页
Computer Science
基金
国家自然科学基金项目(60973084)
广东省自然科学基金项目(9151064101000106)资助
