Improving of image edge detection technique

doi:10.3778/j.issn.1002-8331.2010.18.051

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (18): 164-166.DOI: 10.3778/j.issn.1002-8331.2010.18.051

• 图形、图像、模式识别 • Previous Articles Next Articles

Improving of image edge detection technique

LI Dan^1，2，QIAN Jian-sheng¹，LU Nan-nan¹，ZHOU Jun-min³，TIAN Wen-ke⁴

1.College of Information and Electric Engineering，China University of Mining and Technology，Xuzhou，Jiangsu 221008，China
2.College of Xuhai，China University of Mining and Technology，Xuzhou，Jiangsu 221008，China
3.Lu’neng Heze Coal and Electricity Exploitation Company，Heze，Shandong 274000，China
4.Wangzhuang Coal Mine，Lu’an Huanneng Stock Company，Lu’an，Shanxi 046204，China

Received:2009-03-06 Revised:2009-06-02 Online:2010-06-21 Published:2010-06-21
Contact: LI Dan

图像边缘检测技术的改进

厉丹^1，2，钱建生¹，芦楠楠¹，周均民³，田文科⁴

1．中国矿业大学信息与电气工程学院，江苏徐州 221008
2．中国矿业大学徐海学院，江苏徐州 221008
3.鲁能菏泽煤电开发有限公司，山东菏泽 274000
4.潞安环能股份公司王庄煤矿，山西潞安 046204

通讯作者: 厉丹

Abstract

Abstract: Compared with some traditional edge detection technique such as Roberts operator，Sobel operator，Gauss－Laplace operator，Canny operator，etc.This paper indicates the traditional algorithms that have drawbacks in the detection accuracy and the antinoise performance.After analyzing the limitation of the above operators，a new method is put forward.On researching for best boundary through iteration arithmetic，it enhances contrast of object and background，object edge，and abstracted object area exactly.It reduces influence of noise to gray threshold in combined with morphology.The results of the experiment pictures indicate that the improved algorithm has the better accuracy，noise against quality，and precision in the edge detection.

Key words: image processing, edge detection, iteration, erosion

摘要： 比较了Roberts算子、Sobel算子、Gauss－Laplace算子、Canny算子等几种传统的边缘检测技术，指出这些算法在边缘检测精度和抗噪声性能方面存在一定的问题。在分析以上算子的缺陷后提出改进的方法。通过迭代算法寻找最佳阈值，增强了目标和背景的对比和目标边缘，准确提取目标区域，并结合形态学进行轮廓提取，有效减少噪声对灰度门限值的影响，通过对实验图像的分析表明，改进的检测算法对图像边缘提取具有较好的检测精度，抗噪能力和准确性。

关键词: 图像处理, 边缘检测, 迭代, 腐蚀

CLC Number:

TP391

LI Dan^1，2，QIAN Jian-sheng¹，LU Nan-nan¹，ZHOU Jun-min³，TIAN Wen-ke⁴. Improving of image edge detection technique[J]. Computer Engineering and Applications, 2010, 46(18): 164-166.

厉丹^1，2，钱建生¹，芦楠楠¹，周均民³，田文科⁴. 图像边缘检测技术的改进[J]. 计算机工程与应用, 2010, 46(18): 164-166.

[1]	ZHANG Bo, XU Liming, HUANG Zhiwei, YAO Xiaopeng. Multi-objective GANs Pareto Optimality Algorithm Using Gradient Strategy [J]. Computer Engineering and Applications, 2021, 57(9): 89-95.
[2]	RAN Rong, XU Xinghua, QIU Shaohua, CUI Xiaopeng, OUYANG Bin. Review of Crack Detection Methods Based on Deep Convolutional Neural Networks [J]. Computer Engineering and Applications, 2021, 57(9): 23-35.
[3]	YANG Peiwei, ZHOU Yuhong, XING Gang, TIAN Zhiqiang, XU Xiayu. Applications of Convolutional Neural Network in Biomedical Image [J]. Computer Engineering and Applications, 2021, 57(7): 44-58.
[4]	HU Wentao, CHEN Xiuhong. Low-Rank Projection Learning Based on Neighbor Graph [J]. Computer Engineering and Applications, 2021, 57(7): 209-214.
[5]	MA Qinglu, TANG Xiaoyao. Vehicle Detection Algorithm Combining Time Domain and Watershed Information [J]. Computer Engineering and Applications, 2021, 57(24): 227-233.
[6]	LU Wei, LIU Dan, SHAO Min, WU Yangdong. Application of Improved Mask R-CNN Network in Medical Image Recognition and Segmentation [J]. Computer Engineering and Applications, 2021, 57(24): 234-241.
[7]	ZHANG De, LIN Qingyu, GUO Maozu. Review of Single Image Super-Resolution Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(22): 28-41.
[8]	QI Xiaoxiang, LI Min, ZHU Ying, SONG Yu, DU Weidong. Adaptive Region Segmentation of SAR Image Based on Edge Detection [J]. Computer Engineering and Applications, 2021, 57(22): 232-240.
[9]	LI Xiangxia, XIE Xian, LI Bin, YIN Hua, XU Bo, ZHENG Xinwei. Application of Generative Adversarial Networks in Medical Image Processing [J]. Computer Engineering and Applications, 2021, 57(18): 24-37.
[10]	LIN Benfeng, WANG Cheng, SUN Yuecheng. Switch State Detection Method Based on LSD Algorithm and Deep Learning [J]. Computer Engineering and Applications, 2021, 57(17): 181-189.
[11]	XU Yangyang，LI Wei，WANG Jie. Optimization of Robot Delay Using Events and Deadline Control [J]. Computer Engineering and Applications, 2021, 57(17): 260-268.
[12]	ZHANG Wu, ZHOU Xingyu, ZOU Junhua, PAN Zhisong, DUAN Yexin, CHEN Jun. Adversarial Attack Algorithm Based on Erosion Batch Normalization [J]. Computer Engineering and Applications, 2021, 57(16): 116-124.
[13]	XIANG Yixuan, JIANG He, PAN Pinchen, SUN Conghui. Study on [K]-means Clustering Algorithm of Quadratic Power Coupling [J]. Computer Engineering and Applications, 2021, 57(14): 95-102.
[14]	CHEN Shaojie, ZHAO Gansen, LIN Chengchuang, PENG Jing, HUANG Kaixin, LI Zhuangwei, HUANG Runhua, DU Jiahua, FAN Xiaomao. Survey of Application of Deep Learning in Chromosome Segmentation [J]. Computer Engineering and Applications, 2021, 57(11): 46-56.
[15]	HU Wentao, CHEN Xiuhong. Robust Sparse Subspace Learning Based on Locality Preserving Projections [J]. Computer Engineering and Applications, 2021, 57(10): 194-199.

Improving of image edge detection technique

图像边缘检测技术的改进

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics