|Table of Contents|

Adaptive double threshold modified edge detection algorithmfor boot filtering(PDF)


Research Field:
Publishing date:


Adaptive double threshold modified edge detection algorithmfor boot filtering
Xu LelingHu Shi
Chizhou Vocational and Technical College,Chizhou 247000,China
boot filtering adaptive double threshold edge detection
The edge detection is one of the important aspects of image processing. The traditional edge detection method includes two kinds of methods,the method based on the template matching and the method based on the image gradient. In order to overcome the shortages that the template matching method losses more edge information and the image gradient method is easily affected by the noise,an adaptive double threshold modified Kirsch edge detection algorithm is proposed for the boot filtering. For the local information feature of the image,the boot filtering function is dynamically generated at different edge positions of the image to maintain and enhance the edge effect. At the same time,the complex computation of the Kirsch operator is simplified,and the two thresholds are adaptively selected according to the image edge region threshold.The combination of the two thresholds can effectively improve the accuracy and the efficiency of the edge detection algorithm. The experimental results show that,compared with the traditional Kirsch algorithm and the Sobel algorithm,the improved algorithm is better in the edge localization and operation speed,and the processing speed of the image is more than 4 times of the traditional’s while the fine real edge is well detected.


[1] 贺国旗,凌凤彩,林晓. 基于Canny思想的Robinson边缘检测算法[J]. 计算机工程与科学,2016,38(4):755-760.
He Guoqi,Ling Fengcai,Lin Xiao. A Robinson edge detection algorithm based on Canny idea[J]. Computer Engineering & Science,2016,38(4):755-760.
[2]李敏花,柏猛,吕英俊. 自适应阈值图像边缘检测方法[J]. 模式识别与人工智能,2016,29(2):177-184.
Li Minhua,Bai Meng,Lv Yingjun. Adaptive thresholding based edge detection approach forimages[J]. Pattern Recognition and Artificial Intelligence,2016,29(2):177-184.
[3]娄联堂,吴高林. 基于图像噪声检测的平滑方法研究[J]. 中南民族大学学报(自然科学版),2017,36(4):137-142.
Lou Liantang,Wu Gaolin. Research on smoothing method based on image noise detection[J]. Journal of South-Central University for Nationalities(Natural Science Edition),2017,36(4):137-142.
[4]康牧,周震,林晓. Canny思想和Kirsch算法相结合的边缘检测算法[J]. 河南大学学报(自然科学版),2017,47(1):73-78.
Kang Mu,Zhou Zhen,Lin Xiao. An edge detection algorithm based on Kirsch method and Canny idea[J]. Journal of Henan University(Natural Science),2017,47(1):73-78.
[5]许宏科,秦严严,陈会茹. 一种基于改进Canny的边缘检测算法[J]. 红外技术,2014(3):210-214.
Xu Hongke,Qin Yanyan,Chen Huiru. An improved algorithm for edge detection based on Canny[J]. Infrared Technology,2014(3):210-214.
[6]凌凤彩,康牧,林晓. 改进的Canny边缘检测算法[J]. 计算机科学,2016(8):309-312.
Ling Fengcai,Kang Mu,Lin Xiao. Improved Canny edge detection algorithm[J]. Computer Science,2016(8):309-312.
[7]董昱,高云波,刘翔. 改进的遗传算法在Canny算子阈值选取中的应用[J]. 兰州交通大学学报,2014(6):1-5.
Dong Yu,Gao Yunbo,Liu Xiang. Application of improved genetic algorithm in threshold selection of Canny operator[J]. Journal of Lanzhou Jiaotong University,2014(6):1-5.
[8]齐丹阳,蒋峥,陈毅,等. 一种改进边缘连接的Canny边缘检测算法[J]. 武汉科技大学学报,2014,37(4):310-314.
Qi Danyang,Jiang Zheng,Chen Yi,et al. An improved edge linking algorithm for Canny edge detection[J]. Journal of Wuhan University of Science and Technology,2014,37(4):310-314.
[9]李洪安,张飞,杜卓明,等. 针对合成孔径雷达图像的新型LOG边缘检测算法[J]. 图学学报,2015,36(3):413-417.
Li Hongan,Zhang Fei,Du Zhuoming,et al. A new LOG edge detection algorithm based on synthetic aperture radarimage[J]. Journal of Graphics,2015,36(3):413-417.
[10]Wu Heyu,Li Lu,Xing Congcong,et al. A new method of edge detection based on the total horizontal derivative and the modulus of full tensor gravity gradient[J]. Journal of Applied Geophysics,2017,139(3):246-256.
[11]Hai Hoang-Hong,Chen Liang-Chia,Nguyen Duc Trung,et al. Accurate submicron edge detection using the phase change of a nano-scale shifting laser spot[J]. Optics and Laser Technology,2017,92(4):109-119.
[12]陈家益,黄楠,熊刚强,等. 基于置信区间的自适应加权均值滤波算法[J]. 南京理工大学学报,2017,41(3):307-312.
Chen Jiayi,Huang Nan,Xiong Gangqiang,et al. Adaptive weighted mean filtering algorithm based on confidence interval[J]. Journal of Nanjing University of Science and Technology,2017,41(3):307-312.
[13]刘先红,陈志斌. 基于多尺度方向引导滤波和卷积稀疏表示的红外与可见光图像融合[J]. 光学学报,2017,37(11):111-120.
Liu Xianhong,Chen Zhibin. Fusion of infrared and visible images based on multi-scale directional guided filter and convolutional sparse representation[J]. Acta Optica Sinica,2017,37(11):111-120.
[14]张琳梅,赵莉. 改进Canny的核磁共振图像伪影校正算法[J]. 南京理工大学学报,2016,40(1):50-55.
Zhang Linmei,Zhao Li. Correction algorithm of artifact in magnetic resonance imaging based on improved Canny[J]. Journal of Nanjing University of Science and Technology,2016,40(1):50-55.
[15]宋爽,任洪娥,官俊. 基于Sobel梯度模板的多阈值实时边缘检测方法[J]. 计算机工程与应用,2015,51(23):199-202.
Song Shuang,Ren Honge,Guan Jun. Multi-threshold and read-time edge detection method based on Sobel gradient template[J]. Computer Engineering and Application,2015,51(23):199-202.


Last Update: 2018-04-30