基于多尺度多方向结构元素的形态学图像边缘检测算法

J4 ›› 2017, Vol. 34 ›› Issue (3): 278-285.

基于多尺度多方向结构元素的形态学图像边缘检测算法

吴朔媚,韩明,王敬涛

1石家庄学院计算机科学与工程学院，河北石家庄 050035； 2燕山大学信息科学与工程学院，河北省计算机虚拟技术与系统集成重点实验室，河北秦皇岛 066004

收稿日期:2016-09-30 修回日期:2016-12-08 出版日期:2017-05-28 发布日期:2017-05-22
通讯作者: 吴朔媚（1977-）,女，河北邢台人，硕士，讲师，研究方向为图像处理与测量。Email：shuore@sohu.com
基金资助:
Supported by Science and Technology Planning Projects of Hebei Province(河北省科技计划,16222101D,1522032）

Morphology image edge detection algorithm based on multi-scale and multi-direction structure elements

1 Institute of Computer Science and Engineering, Shijiazhuang University, Shijiazhuang 050035, China； 2 Key Laboratory of Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinghuangdao 066004, China

Received:2016-09-30 Revised:2016-12-08 Published:2017-05-28 Online:2017-05-22

摘要/Abstract

摘要： 为进一步改善图像处理中的噪声抑制和边缘检测性能，提出了一种多尺度多方向结构元素形态学图像边缘检测算法。该算法基于数学形态学中结构元素的方向性差异，充分利用了腐蚀、膨胀、开、闭及其变换和组合运算。对图像进行去噪、边缘提取等预处理操作，以提高图像的信噪比和边缘细节；利用递归的多尺度多方向结构元素形态学滤波得到图像的初始轮廓；利用多尺度形态学和多方向结构元素进行图像边缘检测。实验结果表明提出的算法抗噪性强，能有效准确地提取边缘信息。

关键词: 图像处理；多尺度多方向；数学形态学；边缘检测；结构元素

Abstract: In order to further improve the performance of noise suppression and edge detection in image processing, a new morphology image edge detection algorithm based on multi-scale and multi-direction structure elements is proposed. The method fully utilizes the erosion, dilation, opening，closing and its transformation and combination operation based on the directional differences of structural elements in mathematical morphology. In order to improve the signal-to-noise ratio and edge details of image, the image de-noising, edge extraction and other preprocessing operations are carried out. The image initial contour is obtained by using multi-scale and multi-direction structure elements recursive morphological filtering. Image edge detection is carried out by using multi-scale morphology and multi-direction structure elements. Experimental results show that the proposed algorithm has more powerful anti-noise performance and can extract the edge information effectively and accurately.

Key words: image process; multi-scale and multi- direction; mathematical morphology; edge detection; structure element

中图分类号:

TP391

吴朔媚韩明王敬涛. 基于多尺度多方向结构元素的形态学图像边缘检测算法[J]. J4, 2017, 34(3): 278-285.

参考文献

[1] CHACON-MURGUIA M I, PEREZ-VARGAS F J. Thermal video analysis for fire detection using shape regularity and intensity saturation features[C]. Lecture Notes in Computer Science, Cancun, Mexico, 2011, 6718: 118-126.
[2] Chang Chuan Yu. Contextual-based hopfield netural network for medical image edge etection[J]. Optical Engineering, 2009, 45(3): 1906-1915.
[3] Li Tian-gang, Wang Su-pin, Zhao Na. Gray-scale edge detection for gastric tumor pathologic cell images by morphological analysis[J] . Computers in Biology and Medicine, 2009, 39( 11) : 947-952
[4] Wang Lin Lin, Yang fan, WANG zuo cheng, Adaptive multi-directions algorithm of fuzzy mathematical morphology for edge detection[J]. Application Research of Computers, 2009, 26( 3) : 1177-1179.
[5] JACOPO G，PIERRE S． Edge-preserving smoothing using a similarity measure in adaptive geodesic neighborhoods[J]. Pattern Recognition, 2009, 42(10) : 2306-2316.
[6] Mukhopadhyay S , Chanda B. An edge preserving noise smoothing technique using multi-scale morphology[J].Signal Processing, 2002 , 82 :527 -544 .
[7] Hou Zhi-qiang, Han Chong-zhao, Zuo Dong-guang, et al. Edge Detection Algorithm Based on Local Multiple Morphological Structuring. Journal of Xi’An Jiao Tong University. 2003,37(4):439-440
侯志强, 韩崇昭, 左东广,等. 基于局部多结构元素数学形态学的灰度图像边缘检测算法[J]. 西安交通大学学报, 2003, 37(4): 439-440.
[8] Thouraya M, Malika B, Bachir B. Mathematical morphology for TOFD image analysis and automatic crack detection[J]. Ultrasonics, 2014,54(6):133-141.
[9] Han Ming, Liu Jiao-min, Meng Jun-ying, et al. A Modeling and Target Detection Algorithm Based on Adaptive Adjustment K-ρ for Mixture Gaussian Background[J]. Journal of Electronics & Information Technology, 2014, 36(8):2023-2027.
韩明, 刘教民,孟军英,等.一种自适应调整K-ρ的混合高斯背景建模和目标检测算法[J].电子与信息学报, 2014, 36(8):2023-2027.
[10] Xie Xiao-meng. Research on key techniques of moving object detection and recognition in complex background[D]. Guangzhou: South China University of Technology, 2012: 1-30 ( in Chinese)
解晓萌. 复杂背景下运动目标检测和识别关键技术研究[D].广州:华南理工大学, 2012: 1-30．

[1]	於康杰, 方波, 李剑敏, 王震, 蔡晋辉, 邬佳璐, 何正龙 . 太赫兹成像三维块匹配自适应Canny边缘检测算法[J]. 量子电子学报, 2023, 40(4): 458-468.
[2]	陈建明, , 曾祥津, , 钟丽云, , 邸江磊, ∗ , 秦玉文, ∗. 基于深度学习的图像配准方法研究进展综述[J]. 量子电子学报, 2022, 39(6): 899-926.
[3]	王之润 , 何鹏 , 赵文静 , 翟爱平 , 王东, ∗. 软硬件协同实现 DQN-Hadamard 单像素成像的研究[J]. 量子电子学报, 2022, 39(6): 962-972.
[4]	杨一杉, 梁华为, 姚瑶, 陈帅∗. 金属遮挡条件下光子层析快速重构方法研究[J]. 量子电子学报, 2022, 39(4): 558-565.
[5]	张杨∗, 程正东, 朱斌. 基于改进 Faster R-CNN 的无人机小目标检测算法[J]. 量子电子学报, 2022, 39(3): 354-363.
[6]	毛仁祥, 常建华, 张树益李红旭, 张露瑶. 基于3D-MobileNetv2 的多目标实时跟踪框架[J]. 量子电子学报, 2022, 39(3): 364-372.
[7]	臧一鸣, 朱尚超, 魏战红∗, 刘志飞, 林小竹, 孙文韬. 一种量子图像伪彩色编码方法[J]. 量子电子学报, 2022, 39(3): 343-353.
[8]	王昱皓, 唐泽恬, 钟岷哲, 王阳, 曾瑞敏, 朱登玮, 杨晨. 基于相位相关和纹理分类的SIFT 图像拼接算法[J]. 量子电子学报, 2020, 37(6): 650-658.
[9]	唐泽恬丁召曾瑞敏钟岷哲朱登玮王昱皓王阳杨晨. 基于动态阈值和全局信息的SIFT量子图像拼接[J]. 量子电子学报, 2020, 37(1): 22-28.
[10]	汪炜怡, 徐青山, 杨明翰. 基于光学灰度图像辨识的系统故障诊断方法[J]. 量子电子学报, 2019, 36(6): 699-704.
[11]	刘斌郑凯凯. 基于四通道不可分小波的均值漂移目标跟踪方法[J]. J4, 2018, 35(1): 13-22.
[12]	黄金国，周先春. 基于交叉控制机制与多方向折叠异扩散的图像加密算法[J]. J4, 2018, 35(1): 23-30.
[13]	廖斌訚鹏. 基于递归双边滤波的快速图像去雾[J]. J4, 2017, 34(6): 661-671.
[14]	廖斌苏涛. 基于窄带优化的自适应多匹配块随机查找图像修复[J]. J4, 2017, 34(6): 656-661.
[15]	刘新会桑庆兵. 可控金字塔分解的立体图像质量评价方法[J]. J4, 2017, 34(6): 672-681.