面向多目标透镜的连通域标记检测算法

doi:10.16180/j.cnki.issn1007-7820.2020.04.009

Abstract

Abstract:

A multi-target connected domain labeling suitable algorithm for FPGA was proposed in the study. By making a special mark value at the edge of the image during the first scanning, the algorithm extracted ring inner region with inner and outer double-layer contours object to separate the close objects for multi-target detection. The scheme adopted a single pixel-by-pixel scanning method to perform one scanning of the image, and could accomplish the mark of connected regions and the sorting of equivalent tables. Experimental results showed that for an image with the size of M×N, without calculating the recursive time of the equivalent table, multiple targets could be identified after M×N×2 clock cycles. In addition, parameters including the area and the outer rectangle corresponding to each target were finally obtained.

Key words: image processing, multi-target image, connected domain markers, FPGA, machine vision, binary image

CLC Number:

TP391

XIE Zhixuan,YAO Hongbing,FAN Ning,CHEN Feng. Connected Domain Label Detection Algorithm for Multi-target Lens[J].Electronic Science and Technology, 2020, 33(4): 50-54.

Figures/Tables 10

Figure 1.

Figure 2.

Figure 3.

Table 1

Figure 4.

Figure 5.

Figure 6.

Table 2

Table 3

Figure 7.

References 16

[1]	Telljohann A. Introduction to building a machine vision inspection,handbook of machine vision[M]. Darmstadt: Wiley Press, 2007.
[2]	刘紫燕, 唐虎, 冯亮 . 基于FPGA的机器视觉技术视频跟踪系统实现[J]. 电子科技, 2018,31(9):41-49.
	Liu Ziyan, Tang Hu, Feng Liang . Realization of video tracking system for machine vision technology based on FPGA[J]. Electronic Science and Technology, 2018,31(9):41-49.
[3]	Andersson T, Thurley M J, Carlson J E . A machine vision system for estimation of size distributions by weight of limestone particles[J]. Minerals Engineering, 2016,25(1):38-46. doi: 10.1016/j.mineng.2011.10.001
[4]	Jeong J, Lee G, Lee M , et al. Asingle-pass connected component labeler without label merging period[J]. Journal of Signal Processing Systems, 2016,84(2):211-223. doi: 10.1007/s11265-015-1048-7
[5]	张奎, 陈兆学 . 一种基于游程码的二值图像孔洞标记和表达方法[J]. 光学技术, 2017,43(4):364-368.
	Zhang Kui, Chen Zhaoxue . Holes labeling & expressing method in binary images based on run-length code[J]. Optical Technique, 2017,43(4):364-368.
[6]	张国和, 徐快, 段国栋 , 等. 一种适于硬件实现的快速连通域标记算法[J]. 西安交通大学学报, 2018,52(8):95-101.
	Zhang Guohe, Xu Kuai, Duan Guodong , et al. Afast labeling algorithm of connected components applicable for hardware implementation[J]. Journal of Xi'an Jiaotong University, 2018,52(8):95-101.
[7]	Gu Q, Takaki T, Ishii I . Fast FPGA-based multiobject feature extraction[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013,23(1):30-45. doi: 10.1109/TCSVT.2012.2202195
[8]	戴华东, 胡谋法 . 基于连通域标记的目标检测算法设计与实现[J]. 现代电子技术, 2015,38(20):71-74.
	Dai Huadong, Hu Moufa . Design and implementation of target detection algorithm based on connected domain labeling[J]. Modern Electronics Technique, 2015,38(20):71-74.
[9]	马毅超, 陶亚凡, 吴龙胜 , 等. 基于FPGA的二值图片连通区域标记算法[J]. 微电子学与计算机, 2017,34(1):119-122.
	Ma Yichao, Tao Yafan, Wu Longsheng , et al. FPGA-based binary connected component detection and labeling algorithm[J]. Microelectronics and Computer, 2017,34(1):119-122.
[10]	陈小光, 王琳, 汪周武 . 一种基于连通域分析的卡片指纹图像分割算法[J]. 北方工业大学学报, 2015,27(1):1-6.
	Chen Xiaoguang, Wang Lin, Wang Zhouwu . Afast andefficient algorithm for fingerprint segmentation based on connected analysis[J]. Journal of North China University of Technology, 2015,27(1):1-6.
[11]	于潇宇, 郭玉波 . 基于点目标连通域标记的实时特征提取及其分布式运算[J]. 光学学报, 2015,35(2):1-11
	Yu Xiaoyu, Guo Yubo . Real-time point feature extraction based on connected components labeling and distributed computing[J]. Acta Optica Sinica, 2015,35(2):1-11.
[12]	李艳婷, 王水鱼 . 视频图像中动态目标检测系统的FPGA硬件设计[J]. 计算机系统应用, 2017,26(3):119-125.
	Li Yanting, Wang Shuiyu . FPGA hardware design of dynamic target detection system in video image[J]. Computer Systemsand Applications, 2017,26(3):119-125.
[13]	王凯, 黄山, 赵瑜 , 等. 面向图像目标提取的改进连通域标记算法[J]. 计算机工程与设计, 2014,35(7):2439-2441.
	Wang Kai, Huang Shan, Zhao Yu , et al. Improved algorithm of connected component labeling for image targets extraction[J]. Computer Engineering and Design, 2014,35(7):2439-2441.
[14]	赵晓, 何立风, 姚斌 , 等. 一种基于连通域标记的纸病检测算法[J]. 中国造纸学报, 2018,33(2):51-56.
	Zhao Xiao, He Lifeng, Yao Bin , et al. Apaper disease detection algorithm based on connected region marking[J]. Transactions of China Pulp and Paper, 2018,33(2):51-56.
[15]	姑丽祖热·吐尔逊, 尤努斯·艾沙, 吐尔根·依布拉音 , 等. 连通域结合重叠度的维吾尔文档图像文字切分[J]. 计算机工程与设计, 2016,37(7):1892-1897.
	Gulzira Tursun, Yunus Aysa, Tuergin Yibulayin , et al. Combination of connected regions and overlapping degree based Uyghr document image text segmentation[J]. Computer Engineering and Design, 2016,37(7):1892-1897.
[16]	桑红石, 赵慧, 尚社 . 多值图像连通域标记ASIC结构设计[J]. 小型微型计算机系统, 2008,29(1):180-184.
	Sang Hongshi, Zhao Hui, Shang She . Architecture design of ASIC used for connected components labeling of muilt-value image[J]. Journal of Chinese Computer Systems, 2008,29(1):180-184.

数据位	标记参数
0~10	标记值
11~21	X_max
22~32	X_min
33~43	Y_max
44~54	Y_min
55~69	面积

临时标记值	X_max	X_min	Y_max	Y_min	面积
3	269	149	805	694	9 775
89	433	312	770	656	9 861
142	524	429	858	785	5 035
217	687	593	871	793	5 150

Connected Domain Label Detection Algorithm for Multi-target Lens

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 16

Related Articles 15

Metrics

Comments

Recommended 10

[1]	ZONG Shengkang,CHENG Jianpeng,ZHANG Xiliang. Automatic Detection Method of Crane Track Altitude Difference Based on Spot Position [J]. Electronic Science and Technology, 2022, 35(1): 21-28.
[2]	MA Ding,WU Dingxiang,TANG Lijun. Research on the Defect Method of Detection Circular Ceramic Metal Coating [J]. Electronic Science and Technology, 2021, 34(6): 28-33.
[3]	YANG Lingxiao,WANG Zhenying,LIU Qunpo,WANG Gaowei,YANG Yanchao. Study of Apple Coloration Detection Algorithm Based on H-S Histogram [J]. Electronic Science and Technology, 2021, 34(5): 13-17.
[4]	SONG Zhangming,HE Huiyong,HUANG Yuejun. Study on On-Line Detection of Surface Defects of Flat Enameled Wire [J]. Electronic Science and Technology, 2021, 34(5): 72-78.
[5]	YAN Zijie,WANG Jingmei,CHEN Zhuo,LIU Yu. Implementation of Software Programmable FPGA Network Measurement Engine Technology [J]. Electronic Science and Technology, 2021, 34(2): 27-32.
[6]	YAN Chao,SUN Zhanquan,TIAN Engang,ZHAO Yangyang,FAN Xiaoyan. Research Progress of Medical Image Segmentation Based on Deep Learning [J]. Electronic Science and Technology, 2021, 34(2): 7-11.
[7]	CHEN Wenyou,ZHANG Wei,SHI Xiaofan,SONG Fang. A Semi-Dense 3D Reconstruction ORB-SLAM Algorithm with Improved ORB Feature Matching [J]. Electronic Science and Technology, 2021, 34(12): 62-67.
[8]	QIN Xing,GAO Xiaoqi,CHEN Bin. Image Super-resolution Algorithm Based on SqueezeNet Convolution Neural Network [J]. Electronic Science and Technology, 2020, 33(5): 1-8.
[9]	SHI Weizhong,CAO Weiwei,FAN Yanming,DONG Jiajun,CHEN Shu,XIAO Hao. FPGA-Based Real-Time Edge Detection and its Implementation for Deep-Space Images [J]. Electronic Science and Technology, 2020, 33(5): 45-49.
[10]	ZHANG Tianhao,LI Haolin. A Method of Laser Center Extraction Based on Gaussian Fitting [J]. Electronic Science and Technology, 2020, 33(5): 72-76.
[11]	ZHAN Yan,ZHANG Juan. A Domain Adaptive Depth Estimation Method for Structural Perception Loss [J]. Electronic Science and Technology, 2020, 33(12): 12-16.
[12]	ZHAO Nengwu,LU Hongmin,XU Tao,HU Kuan,HE Chuanxia,MENG Xiaojiao. Design of Signal Source for Perimeter Intrusion Detection System Based on AD9910 [J]. Electronic Science and Technology, 2020, 33(1): 1-5.
[13]	DUAN Tie,HUANG Yan,WANG Yang. Reconfiguration Design of Digital PLL in Carrier Synchronous [J]. Electronic Science and Technology, 2019, 32(8): 41-45.
[14]	YAN Maosen,CHEN Jiaqi. A Multi-environmental Traffic Sign Recognition Model [J]. Electronic Science and Technology, 2019, 32(4): 68-72.
[15]	LUO Ying,WU Qiang,QIN Yun. The Least Mean Square Ultrasonic Beamforming Algorithm Based on GSC [J]. Electronic Science and Technology, 2019, 32(2): 37-41.