首页 > 过刊浏览>2017年第31卷第6期 >827-832. DOI:10.13382/j.jemi.2017.06.002
PDF HTML阅读 XML下载 导出引用 引用提醒
自适应粒子群集优化二维OSTU的图像阈值分割算法
作者:
作者单位:

1. 沈阳理工大学自动化与电气工程学院沈阳110159;2. 沈阳理工大学信息科学与工程学院沈阳110159

中图分类号:

TP391.41;TN911.73

基金项目:

辽宁省自然科学基金(201602652)资助项目


Image threshold segmentation algorithm based on adaptive particle swarm optimization of twodimensional OSTU
Author:
Affiliation:

1. School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China; 2. School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [17]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    为了解决红外相机采集行人图片时图像分割效果问题，提出一种自适应粒子群优化二维OSTU的阈值分割算法。利用当前帧图像的灰度级和当前帧图像像素的邻域灰度级构成二元组，通过计算二者的均值和方差，建立二维最大类间方差模型，结合自适应粒子群集算法，估计出图像的最佳阈值，该方法不仅能够准确估计阈值且计算时间减少。仿真结果表明，阈值最佳时，当结合自适应粒子群集优化算法后计算时间减少到原来的50%，所提出的算法能够快速准确得到最佳阈值，提高了图像预处理的分割效果。

    Abstract:

    In order to solve the effect of the image segmentation when the pedestrian image is collected by infrared camera, an image threshold segmentation algorithm based on adaptive particle swarm optimization of twodimensional OSTU is utilized. The gray scale of the current frame image and the neighborhood gray level of the current frame image pixel form a binary image. A 2D maximum betweencluster variance model is built up through calculating the average and variance between them, and combining with adaptive particle swarm optimization algorithm the best threshold image value is estimated. The algorithm can accurately estimate the threshold and reduce the calculation time. The simulation results demonstrate that the best image value is proper, the calculation time is shortened 50% when combine with adaptive particle swarm optimization algorithm. The proposed algorithm can get the optimal threshold quickly and accurately, and improve the segmentation effect of image preprocessing.

    参考文献
    [1]范晋祥，杨建宇.红外成像探测技术发展趋势分析[J].红外与激光工程, 2012, 41(12):31453153. FAN J X，YANG J Y.Development trends of infrared imaging detecting technology[J].Infrared and Laser Engineering，2012,41(12):31453153.
    [2]龚淑柯，吴铁军.基于三维直方图的Fisher评价函数图像分割方法[J].电光与控制，2005,1212(4):3537. GONG SH K，WU T J.3D histogram based Fisher criterion function for image segmentation[J].Electronics Optics & Control，2005,12(4):3537.
    [3]朱贺，李臣明，张丽丽，等.联合灰度阈值分割及轮廓形态识别的河道提取[J].电子测量与仪器学报， 2014,28(11):12881296. ZHU H,LI CH M,ZHANG L L,et al.River channel extraction by combining grey threshold segmentation and contour form recognition[J].Journal of Electronic Measurement and Instrumentation,2014，28(11):12881296.
    [4]王慧倩，邹永宁，蔡玉芳，等.基于Hessian矩阵和熵的CT序列图像裂缝分割方[J].仪器仪表学报，2016,37(8):18001807. WANG H Q,ZOU Y N,CAI Y F,et al.Crack segmentation in CT image sequences using Hessian matrix and entropy[J].Chinese Journal of Scientific Instrument，2016, 37(8):18001807.
    [5]吴艳丽，赵德群，陈鹏宇.一种凝胶电泳图像的预处理方法[J].国外电子测量技术，2016, 35(11):5357. WU Y L,ZHAO D Q,CHEN P Y.Preprocessing method of gel electrophoresis image[J].Foreign Electronic Measurement Technology,2016,35(11):5357.
    [6]XU F L,LIU X,FUJIMURA K.Pedestrian detection and tracking with night vision[J].IEEE Transactions on Intelligent Transportation System，2005,6(1):6371.
    [7]云挺进，郭永彩.高潮.K均值聚类中心分析法实现红外人体目标分割[J].光电工程，2008,35(3)：140144. YUN T J,GUO Y C,GAO CH.Human segmentation algorithm in infrared images based on Kmeans clustering centers aalysis[J].OptoElectronic Engineering，2008,35(3):140144.
    [8]LIU Q,ZHUANG J J.A generalized thresholding algorithm of pedestrian segmentation for farinfrared images[C].Proceedings of IEEE International Conference on Imaging Systems and Techniques(IST),IEEE，2012:338343.
    [9]GE J F, LUO Y P, GYOMEI T. Realtime pedestrian detection and tracking at nighttime for driverassistance systems [J].IEEE Transactions on Intelligent Transportation Systems，2009,10(2): 283298.
    [10]吴一全，孟天亮，吴诗婳.图像阈值分割方法研究进展20年（1994—2014）[J]数据采集与处理,2015,30(1):123. WU Y Q,MENG T L,WU SH H.Research progress of image thresholding methods in recent 20 years(19942014)[J].Journal of Data Acquisition and Processing,2015,30(1)：123.
    [11]ADILJAN Y,YOSHIHIRO H,TASUKU M,et al.2D direction histogram based entropic thresholding[J].Neurocomputing,2013,120(10):287297.
    [12]王星，宗凯.基于局部熵的图像分割算法研究与实现[J].电子测量技术，2016, 39(12):168171. WANG X,ZONG K.Research and implementation of imageSegmentation algorithm based on local entropy[J].Electronic Measurement Technology,2016,39(12):168171.
    [13]马英辉，吴一全.基于二维Renyi交叉熵的刀具磨损图像分割[J].电子测量与仪器学报，2016,12，30(12):18691876. MA Y H,WU Y Q.Image segmentation for tool wear on 2D Renyi cross entropy[J].Journal of Electronic Measurement and Instrumentation，2016, 30(12):18691876.
    [14]DU F,SHI W K,CHEN L ,et al.Infrared image segmentation with 2Dmaximum entropy method based on pattile swarm optimization[J].Pattern Recognition Letters，2005，26(5):597603.
    [15]KARABOGA D.An idea based on honey bee swarm for numerical optimization[D]. Keyseri: Ereiyes University，2005.
    [16]韩江洪, 李正荣, 魏振春.一种自适应粒子群优化算法及其仿真研究[J].系统仿真学报，2006,18(10):29692971. HAN J H,LI ZH R,WEI ZH CH.Adaptive particle swarm optimization algorithm and simulation[J].Journal of System Simulation,2006, 18(10):29692971.
    [17]ZHANG J,HU J L.Image segmentation based on 2DOSTU method with histogram analysis[C].International Conference on Computer Science and Software Engineering,IEEE，2008:105108.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

于洋,孔琳,虞闯.自适应粒子群集优化二维OSTU的图像阈值分割算法[J].电子测量与仪器学报,2017,31(6):827-832

复制
分享
文章指标
  • 点击次数:3706
  • 下载次数: 16636
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 在线发布日期: 2017-08-02
文章二维码
您是第  5554410   位访问者
主管单位：中国科学技术协会
主办单位：中国电子学会
地址：北京市东城区北河沿大街79号
电话：010-53389339
电子测量与仪器学报 ® 2025 版权所有