登录
首页 » matlab » yudong

yudong

于 2010-04-12 发布 文件大小:829KB
0 181
下载积分: 1 下载次数: 1

代码说明:

说明:  运动目标识别,采用背景差分方法。背景图像由均值法获得。里面的视频换成前几帧换成纯背景效果能更好一点(Moving target identification, using the background difference method. Background image obtained from the mean. Which replaced the previous video frame replaced by pure background a little better results)

文件列表:

yudongguji\main.asv
yudongguji\main.m
yudongguji\slprj\grt\vipmotion_win\tmwinternal\binfo.mat
yudongguji\slprj\grt\vipmotion_win\tmwinternal\minfo.mat
yudongguji\slprj\modeladvisor\vipmotion__win\Info\folder.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\folder_failed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\folder_pass.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\folder_warning.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\geninfo.mat
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_folder.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_procedure.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_task.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_task_disabled.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_task_pselected.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\icon_task_required.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\info_icon.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\mdladvinfo.mat
yudongguji\slprj\modeladvisor\vipmotion__win\Info\minus.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\model_diagnose.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\model_diagnose_custom.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\model_diagnose_left.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\model_diagnose_task.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\model_diagnose_top.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\plus.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\procedure_failed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\procedure_passed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\procedure_warning.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\report.html
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_failed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_forcepass.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_passed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_req_forcepassed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_req_passed.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\task_warning.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\vandv.png
yudongguji\slprj\modeladvisor\vipmotion__win\Info\vandvback.png
yudongguji\slprj\sl_proj.tmw
yudongguji\temp4.avi
yudongguji\test.m
yudongguji\test1.m
yudongguji\vipmotion_win.exe
yudongguji\vipmotion_win_grt_rtw\buildInfo.mat
yudongguji\vipmotion_win_grt_rtw\defines.txt
yudongguji\vipmotion_win_grt_rtw\grt_main.obj
yudongguji\vipmotion_win_grt_rtw\HostLib_MMFile.obj
yudongguji\vipmotion_win_grt_rtw\HostLib_rtw.obj
yudongguji\vipmotion_win_grt_rtw\HostLib_Video.obj
yudongguji\vipmotion_win_grt_rtw\modelsources.txt
yudongguji\vipmotion_win_grt_rtw\rtGetInf.c
yudongguji\vipmotion_win_grt_rtw\rtGetInf.h
yudongguji\vipmotion_win_grt_rtw\rtGetInf.obj
yudongguji\vipmotion_win_grt_rtw\rtGetNaN.c
yudongguji\vipmotion_win_grt_rtw\rtGetNaN.h
yudongguji\vipmotion_win_grt_rtw\rtGetNaN.obj
yudongguji\vipmotion_win_grt_rtw\rtmodel.h
yudongguji\vipmotion_win_grt_rtw\rtwtypes.h
yudongguji\vipmotion_win_grt_rtw\rtwtypeschksum.mat
yudongguji\vipmotion_win_grt_rtw\rtw_proj.tmw
yudongguji\vipmotion_win_grt_rtw\rt_logging.obj
yudongguji\vipmotion_win_grt_rtw\rt_nonfinite.c
yudongguji\vipmotion_win_grt_rtw\rt_nonfinite.h
yudongguji\vipmotion_win_grt_rtw\rt_nonfinite.obj
yudongguji\vipmotion_win_grt_rtw\rt_sim.obj
yudongguji\vipmotion_win_grt_rtw\vipmotion_win.bat
yudongguji\vipmotion_win_grt_rtw\vipmotion_win.c
yudongguji\vipmotion_win_grt_rtw\vipmotion_win.h
yudongguji\vipmotion_win_grt_rtw\vipmotion_win.mk
yudongguji\vipmotion_win_grt_rtw\vipmotion_win.obj
yudongguji\vipmotion_win_grt_rtw\vipmotion_win_data.c
yudongguji\vipmotion_win_grt_rtw\vipmotion_win_data.obj
yudongguji\vipmotion_win_grt_rtw\vipmotion_win_private.h
yudongguji\vipmotion_win_grt_rtw\vipmotion_win_ref.rsp
yudongguji\vipmotion_win_grt_rtw\vipmotion_win_types.h
yudongguji\yudongguji\main.asv
yudongguji\yudongguji\main.m
yudongguji\yudongguji\test.m
yudongguji\yudongguji\test1.m
yudongguji\yudongguji\运动估计.ppt
yudongguji\运动估计.ppt
yudongguji\slprj\grt\vipmotion_win\tmwinternal
yudongguji\slprj\modeladvisor\vipmotion__win\Info
yudongguji\slprj\grt\vipmotion_win
yudongguji\slprj\grt\_sharedutils
yudongguji\slprj\modeladvisor\vipmotion__win
yudongguji\slprj\grt
yudongguji\slprj\modeladvisor
yudongguji\slprj
yudongguji\vipmotion_win_grt_rtw
yudongguji\yudongguji
yudongguji

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • SLIC超素1
    说明:  通过超像素算法,读入图片确定分割数,得到最佳图像分割结果(Through the superpixel algorithm, read in the picture to determine the number of divisions to get the best image segmentation result)
    2020-07-08 15:07:32下载
    积分:1
  • D
    说明:  在MATLAB中实现画无向网络图,可用于社群聚类分析中(In MATLAB,the drawing is not directed to network graph,which can be used in social cluster analysis.)
    2017-07-16 11:30:11下载
    积分:1
  • 代码
    说明:  用jpeg对图像进行编码解码,里面包含界面,以及图片(Using JPEG to encode and decode the image)
    2020-12-22 10:25:59下载
    积分:1
  • recall_precision
    图像处理的查准率和查全率的代码,适合用于图像图像的检测,目标的检测的评估(Image processing precision and recall rate code)
    2013-09-14 10:36:02下载
    积分:1
  • DIPUM_ToolBox
    冈萨雷斯第二版数字图像处理工具箱函数集Matlab源程序(Gonzalez, the second edition of Digital Image Processing Toolbox Matlab source code function set)
    2010-11-15 10:37:22下载
    积分:1
  • SumDifCombineview
    立体图像左右视点融合,将左右图像利用LogGabor融合成一幅图像。(Stereo image fusion, the left and right image are fused into an image using LogGabor.)
    2015-10-14 14:10:26下载
    积分:1
  • 点云配准
    说明:  四种点云配准算法。FPFH,NDT,3DS…(Four point cloud registration algorithms.FPFH,NDT,3DS.)
    2021-04-18 08:58:52下载
    积分:1
  • JPEGCompression
    说明:  编码: (1)进行颜色转换,将RGB格式转换为YUV格式。 (2)将待编码的N×N的图像分解成(N/8)^ 2 个大小为8×8的子图像。 (3)对每个子图像进行DCT变换,得到各子图像的变换系数。这一步的实质是把空间域表示的图像转换成频率域表示的图像。 (4)对变换系数进行量化。 (5)进行Z字形重排 (6)使用霍夫曼变长变码编码器对量化的系数进行编码,得到压缩后的图像(数据)。 解码: (1) 对压缩的图像数据进行解码,得到用量化系数表示的图像数据。 (2) 进行反Z字型重排 (3)用与编码时相同的量化函数或量化值表对用量化系数表示的图像数据进行逆量化,得到每个子图像的变换系数。 (4)对逆量化得到的每个子图像的变换系数进行反向正交变换(如反向DCT变换等),得到(N/8)^2 个大小为8×8的子图像。 (5)将(N/8)^2 个大小为8×8的子图像重构成一个N×N的图像。 (6)进行颜色组合,将YUV格式转换为RGB格式图像。(JPEG compression and decompression process)
    2019-02-18 22:58:13下载
    积分:1
  • SpatialPyramid
    空间金字塔,通过对图片不断的进行层次划分,对每一层的图片信息进行整理用于图片分类等(Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories )
    2013-01-15 16:38:58下载
    积分:1
  • 06026250
    Adaptive Perona–Malik Model Based on the Variable Exponent for Image DenoisingThis paper introduces a class of adaptivePerona–Malik (PM) diffusion, which combines the PM
    2013-09-26 15:38:33下载
    积分:1
  • 696518资源总数
  • 105877会员总数
  • 14今日下载