全景海域图像中的圆形海天线提取

苏丽; 庞迪

doi:10.3788/OPE.20152311.3279

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全景海域图像中的圆形海天线提取

信息科学 | 更新时间：2020-08-13

- 全景海域图像中的圆形海天线提取
- Circle sea-sky-line extraction in panoramic images
- 光学精密工程 2015年23卷第11期页码：3279-3288
- 作者机构：
  
  哈尔滨工程大学自动化学院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51409053)();黑龙江省自然科学基金资助项目(No.E201414,No.F201414)()
- DOI：10.3788/OPE.20152311.3279
  中图分类号： TP391.4
- 收稿日期：2015-08-18，
  
  修回日期：2015-09-29，
  
  纸质出版日期：2015-11-25
- 稿件说明：
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苏丽, 庞迪,. 全景海域图像中的圆形海天线提取[J]. 光学精密工程, 2015,23(11): 3279-3288

SU Li, PANG Di,. Circle sea-sky-line extraction in panoramic images[J]. Editorial Office of Optics and Precision Engineering, 2015,23(11): 3279-3288
苏丽, 庞迪,. 全景海域图像中的圆形海天线提取[J]. 光学精密工程, 2015,23(11): 3279-3288 DOI： 10.3788/OPE.20152311.3279.

SU Li, PANG Di,. Circle sea-sky-line extraction in panoramic images[J]. Editorial Office of Optics and Precision Engineering, 2015,23(11): 3279-3288 DOI： 10.3788/OPE.20152311.3279.

摘要

针对全景海域图像背景复杂且海天线呈近似圆形的特点

提出了一种基于分形维数和改进Hough圆变换的全景海天线提取算法。该算法首先通过地毯覆盖法计算海域全景图像的分形维数

并据此提取出全景采集装置等设备在全景图像中的成像

消除其对海天线检测的不良影响。针对经典的梯度Hough圆变换算法提取全景海天线时无法给出唯一正确解的问题

提出了一种改进的Hough圆变换算法来有效提取全景海天线。对400幅不同情况下采集的全景海域图像进行了实验。结果表明

提出的算法在理想情况、海天线部分缺失、低对比度和海天线大范围断裂等多种情况下均可以有效提取出全景圆形海天线

检测准确率达95.75%

适用性和鲁棒性良好。

Abstract

For a panoramic sea image with complex backgrounds and the sea-sky-line shows as a nearly circle

this paper proposes a panoramic sea-sky-line extraction algorithm based on fractal dimensions and improved Hough circle transform. It firstly calculates the fractal dimensions of the panoramic sea image by blanket-covering method

then extracts the images of panoramic acquisition devices and other equipment to eliminate their adverse effects on sea-sky-line detection based on these fractal dimensions. As the traditional gradient Hough circle transform can not give out a correct solution when it is used to extract the panoramic sea-sky-line

an improved Hough circle transform algorithm is put forward. 400 frame panoramic sea images extracted in different conditions are processed. Experimental results show that this proposed algorithm effectively extracts the circle panoramic sea-sky-lines under conditions of ideal background

lower contrasts images and the sea-sky-line with partial loss or severe breaking and its detection accuracy reaches 95.75%. The applicability and robustness of the proposed algorithm are excellent.

关键词

Keywords

references

陶宏江,金龙旭. 基于各向异性插值的全景图像径向展开算法[J]. 液晶与显示, 2014,29(3):435-440. TAO H J, JIN L X. Unwrapping algorithm based on panoramic image of anisotropic interpolation radial[J]. Liquid Crystals and Displays,2014, 29(3):435-440.(in Chinese)

马相路, 冯莹, 曹毓. 双曲凹面折反射全景成像系统[J]. 红外与激光工程, 2013,42(8):2132-2136. MA X L, FENG Y, CAO Y. Concave hyperboloidal catadioptric omnidirectional imaging system[J]. Infrared and Laser Engineering,2013, 42(8):2132-2136.(in Chinese)

曾文静, 万磊, 张铁栋,等. 复杂海空背景下弱小目标的快速自动检测[J]. 光学精密工程, 2012, 20(2):403-412. ZENG W J, WAN L, ZHANG T D, et al.. Fast detection of weak targets in complex sea-sky background[J]. Opt.Precision Eng., 2012, 20(2):403-412.(in Chinese)

PU H, BO L, REN T T,et al.. A fast sea-level line extraction and object detection method for infrared sea image[C]. International Symposium on Optoelectronic Technology and Application,Beijing, P.R. China:SPIE, 2014, 9300:930007.1-930007.7.

BO L, REN T T,LIU Y B,et al.. Sea-level line extraction based on piecewise line detection[C]. International Symposium on Optoelectronic Technology and Application,Beijing, P.R. China:SPIE, 2014, 9301:93013L.2-93013L.8.

梁世花, 吴巍, 李波, 等. 基于 Seam Carving 的海天线检测算法[J]. 红外与激光工程, 2013,42(10):2817-2821. LIANG SH H, WU W, LI B,et al.. Sea-sky-line detection algorithm based on Seam Carving[J]. Infrared and Laser Engineering,2013, 42(10):2817-2821.(in Chinese)

英向华. 全向摄像机标定技术研究[D]. 沈阳:中国科学院自动化研究所, 2004. YING X H. Study on the Technique of Omnidirectional Camera Calibration[D]. Shenyang:Institute of Automation, Chinese Academy of Sciences, 2004.(in Chinese)

苏丽, 周娜, 徐从营. 基于全景视觉的舰船小目标检测方法研究[C]. 第32届中国控制会议,陕西西安,中国, 2013. SU L, ZHOU N, XU C Y. On the small ship target detection based on panoramic vision[C].Proceedings of the 32nd Chinese Control Conference, Xi'an, P.R. China:CCC, 2013.(in Chinese)

MANDELBROT B B. The Fractal Geometry of Nature[M].New York:W H Freeman and Company, 1982:102-113.

伍妍妮, 潘炼, 王薇. 基于分形特征的复杂环境目标检测方法研究[J]. 计算机测量与控制, 2014,22(5):1327-1329. WU Y N, PAN L, WANG W. A research of moving target detection under complex background based on fractal feature[J]. Computer Measurement & Control, 2014, 22(5):1327-1329.(in Chinese)

耿庆田, 赵宏伟. 基于分形维数和隐马尔科夫特征的车牌识别[J]. 光学精密工程, 2013, 21(12):3219-3204. GENG Q T, ZHAO H W. License plate recognition based on fractal and hidden Markov feature[J]. Opt. Precision Eng., 2013, 21(12):3219-3204.(in Chinese)

于海晶,李桂菊. 基于改进差分盒维数的烟雾分割方法[J]. 液晶与显示, 2013,28(1):115-119. YU H J, LI G J. Smoke segmention method based on improved differential box-counting fractal dimension[J]. Liquid Crystals and Displays,2013,28(1):115-119.(in Chinese)

刘洋, 田小建,王晴,等. 采用局部分形的高效图像分割方法在红外云图处理中的应用[J]. 光学精密工程, 2011,19(6):1367-1374. LIU Y, TAN X J, WANG Q,et al.. Application of efficient image segmentation method based on local fractal in the infrared cloud image processing[J]. Opt. Precision Eng., 2011, 19(6):1367-1374.(in Chinese)

张心心, 顾静良, 何山, 等. 基于分形曲面尺度斜率特征的弱小目标检测[J]. 激光与红外, 2015,45(3):331-334. ZHANG X X, GU J L, HE SH,et al.. Dim target detection based on the scale slope feature of fractal surface[J].LASER & INFRARED, 2015, 45(3):331-334.(in Chinese)

蔡飞, 涂丹. 可见光图像人造目标检测技术综述[J]. 计算机应用研究, 2010,27(7):2430-2434. CAI F, TU D. Survey on man-made object detection in visible imagery[J].Application Research of Computers, 2010, 27(7):2430-2434.(in Chinese)

诸葛霞, 向健勇. 基于分形的实现小目标检测的一种具体方法[J]. 红外技术, 2006, 28(7):411-414. ZHU G X, XIANG J Y.A little target detecting method based on fractal[J].Infrared Technology, 2006, 28(7):411-414.(in Chinese)

MURAKAMI K, ABOSHI M, KINOSHITA K. Fast line detection by Hough transform using inter-image operations[J]. Electronics and Communications in Japan, 2015, 98(7):1-12.

WEI L L, ZHANG X L,FAN L. A TBD algorithm based on improved Randomized Hough Transform for dim target detection[C]. Proceedings of the 2010 2nd International Conference on Signal Processing Systems, Dalian, P.R. China:ICEMS, 2010:V2-241-V2-245.

程鹏, 朱美琳, 耿华. 一种基于梯度Hough变换和 SVM 的圆检测算法[J]. 计算机与现代化, 2013, 2(5):22-26. CHENG P, ZHU M L, GENG H. Circle detection algorithm based on gradient Hough transform and SVM[J].Computer and Modernization, 2013, 2(5):22-26.(in Chinese)

KIMME C, BALLARD D H, SKLANSKY J. Finding circles by an array of accumulators[J]. Communications of the Association for Computing Machinery, 1975, 18(2):120-122.

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