登录
首页 » Others » MFC数字图像处理(BMP格式读取 保存 DFT FFT 直方图 色调均化 缩放 模糊 锐化 滤镜 形态学处理 曲线 裁剪 灰度图 彩色图 自动阈值)

MFC数字图像处理(BMP格式读取 保存 DFT FFT 直方图 色调均化 缩放 模糊 锐化 滤镜 形态学处理 曲线 裁剪 灰度图 彩色图 自动阈值)

于 2020-12-05 发布
0 222
下载积分: 1 下载次数: 1

代码说明:

使用MFC在VS2013编写的数字图象处理软件,能够实现相当强大的功能。BMP格式读取 保存 DFT FFT 直方图 色调均化 缩放 模糊 锐化 滤镜 形态学处理 曲线 裁剪 灰度图 彩色图 自动阈值 等等...除此之外还有很多其他小功能...建议使用VS2013打开!!!核心代码在Bmp.cpp中!!!更新文档:2014年6月18日更新说明:这次应该是上交的最后一次作业了,在今日的展示结束之后总体情况还好,但是发现了几个问题。首先是这个程序是在win8环境下设计的,所以程序的一些大小参数以及按钮图片的位置参数是适合在win8的环境下操作,在设计报告中使用的操作系统也是w

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

  • 求网络的最短路径,直径,介数,度分布, 聚类系数
    可以求的网络的最短路径,直径,介数,度分布, 聚类系数-Can network seek the shortest path, diameter, referred to the number of degree distribution, clustering coefficient
    2020-11-29下载
    积分:1
  • 计算机网络课设计—公司局域网的组建
    公司拥有主机数240台,分布于整座大厦内。该大厦分为5层,其中经理室(主机5台)和人力资源部(主机10台)位于该大厦的顶层,财务部(主机20台)与市场营销部(主机20台)位于4层,其它的部分和楼层分布分别为:网络设计部1部(主机50台)位于3层、网络设计部2部(主机50台)位于2层、培训部(主机50台)位于1层。
    2020-12-09下载
    积分:1
  • 李宏毅GAN对抗生成网络2018最新ppt包括作业指导ppt及相关论文
    李宏毅GAN对抗生成网络2018最新ppt及相关论文,包括作业指导ppt视频地址https://www.bilibili.com/video/av24011528/?p=1博客地址:https://blog.csdn.net/qq_35608277/article/details/83867123
    2020-06-30下载
    积分:1
  • 英汉词典数据 - 单词数据库 (英 转换成 汉 版本)
    包含单词10万余个的电子词典数据。相关下载 (本人资源中可以找到):英汉词典数据 - 单词数据库 (汉字 转换成 英语版本)
    2021-05-06下载
    积分:1
  • HMC833驱动源代码
    HMC833LP6GE芯片驱动源代码,提供技术支持,HMC833/HMC832本人均已调通,可以使用。
    2020-12-04下载
    积分:1
  • 刘金琨机器人控制系统的设计与Matlab仿真-先进设计方法-仿真
    刘金琨老师机器人控制系统的设计与Matlab仿真-先进设计方法-仿真程序,书中是最新的matlab程序代码,可以复现书中的仿真图。
    2020-07-02下载
    积分:1
  • STM32虚拟示波器,通过串口,无需屏幕
    我为了弄飞思卡尔比赛,专门用stm32开发板制作了该虚拟示波器,此示波器,无需LCD屏幕,通过串口调试,在PC端显示波形.方便我们的开发,亲测可用
    2020-11-28下载
    积分:1
  • AGV资料学习参考
    AGV系统,物流自动化AGV中级应用技术全面讲解
    2020-12-02下载
    积分:1
  • 基于高光谱成像的蓝莓内部品质检测 特征波长选择方法研究
    在特征波长选取方面有一些创新,可以作为参考。在特征波长选取方面有一些创新,可以作为参考。(基于高光谱成像的蓝莓内部品质检测特征波长选择方法研究古文君1 ,田有文 1* ,张芳1 ,赖兴涛 1 ,何宽1 ,姚萍1 ,刘博林 2)586-482016620010~15mm0.8~2.3g。fone3:(InSpector V10E, Spectral InFinland)1392pix×1040pixCCDL CCD2(IGV-B141OM, IMPERX Incorporated, USA), 150W1. CCD Camera; 2.Spectrometer; 3.Shot; 4. Light source; 5. Samples(3900 Illuminatior, Illumination Tech6.Translationplatform7.Lightsourcecontroller;8.computernologies inc.,USA)、(IRCP0076-19. Translation platform controllerCOM,)、(120cm×50cmx(DELL VoStro 5560D-1528Figure 1 Schematic diagram of hyperspectral imagingcmsystem400~1000nm,4722.8nmRRGY-4(10mm)(DBR45(successive projections algorithm, SPA(stepwise multiple linear regression, SMLR)(SPA)(SMLR)SPASPASMLRSPA-SPA、SMLR_SMLR、SPA- SMLRSMLR-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5871.6BP(error back propagation)BP17(correlation coeffiient of calibration, Re)(root mean square error of calibration set, RMSEC)correlation coeffiient of pre-diction, Rp)(root mean square error of prediction set, RMSEP)ENVI 4.8(Research System Inc, ), MATLAB 2014a(The Math Works Inc)、TheUnscrambler9.7、 Excel2010(Ⅵ icrosoftdgle banddWcvef.BP models for soluble solidsThe selected characteristic wavelengthCurve of relative reflectanceExtract the region of interescontent and firmness prediction2figure 2 Flow chart of data processing280mm,68ms,28mm·s-。99%202.2600nm600nm2b2c)21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5884823(2f)BPSavitzky-Golasavitzky -golayTable 1 The effect of different spectra preprocessingCalibration setPredictioSpectrum typeRMSECRMSEPOriginal spcctrum0.933/0.9230.3510.4040.9200.9100.508/0.319MSCThe spectrum after MSC processing0.940/0.9450.56lO.3120.9190.9320.516/0.282SNThe spectrum after SNV processin0.93709340.60210.24309220.9010.6320.462Savitzky-golayThe spectrum after Savitzky-Golay processing 0.955/0.9550.3240.2410.951/0.9490.400/0.2782.5SPA-SPA SMLRSMLR SPA-SMLR SMLR-SPASPA-SPASPASavitzky-GolaySPATable 2 The results of multi-stage characteristic wavelength selection methodnmCharacteristie wavelength selection methodSPA-SPA452,455,470,482,490,785,893,912,921,942,950455,470,482,785,893.912SMLR-SMLR457,508,516,534,543,51,556,568,712,720.774,778508,534,543,712,720,774SPA-SMLR452,455,470,482,490,785,893,912,921,942,950452,470,482,490,893,912SMLR-SPA457,508,516,534,543,551,556,568,712,720,774,78534,7202.6Savilzky-gola(FS)392SPA-SPASMLR-SMLRSMLR-SMLRSMLR-SPABPBP0.001500021994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct589BPBPSPA-SPARp RMseP0.9520.391°Brix,RpRMSEP0.9530.234BrixTable 3 Detection results of soluble solid content and firmness of blueberry based on different multi-stagecharacteristic wavelength selection methodsCalibration setPrediction setCharacteristic selection method Wavelength numberRMSECRMSEP3929550.9550.324/0.2410.9510.9490.400/0.278SPA-SPA0.9590.9560.3180.1530.9520.9530.391/0.234SMLR-SMLR0.9560.9340.414/0.243912109020.559/0.349SPA SMLR0.828/0.8581.3670.58582208091.440/0.719SMLR- SPA20.958/0.9360.402/0.3359320.9280.435/0,4041387nm1229nm91.5%BPRRMSEP0.904215.163lBP3Rv0.84V0.94Rv0.83,SEV0.63。400-1000nmSavitzky-GolayBPSPA-SPASPA-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct59048[1 KADER F,ROVEL. B Fractionation and identification of the phenolic compounds of highbush blueberries(Vaccinium corymbosumLUJ].Food Chemistry, 1996,55(1): 35-40「J,2012,33(1):340-342,2017,38(2):301-305.[4 MENDOZA F, LU R, ARIANA D,et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction ofple [ruil firmness and soluble solids conlenl[J] Poslharvesl Biology and Technology, 2011, 62(2: 149-160[5 SUN M J, ZHANG D, LIU L,et al. How to predict the sugariness and hardness of melons a near-infrared [J]. Food Chemistry,2017,218(3:413-42116 SIEDLISKA A, BARANOWSKI P, MAZUREK W, ct al. Classification models of bruise and cultivar detection on the basis of hy-perspectral imaging data[J]. Computers and Electronics in Agriculture, 2014, 106: 66-74[7 LIU D, SUN D W, ZENG X N, el al. Recenl aDvances in wavelength seleclion lechniques for hyperspectral image processing inthe food industry[J]. Food Bioprocess Technol, 2014, 7: 307-323[8 ZHANG C, GUO C T, LIU F,et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector ma-chine[j] Journal of Food Engincering, 2016, 179: 11-18[9J,2016,47(5:634-6402009,29(:1611-1615201536(12)171-17612]J,2012,32(11:3093309[13] LI B C, HOU B L, ZHANG D W,et al. Pears characteristics (soluble solids content and firmness prediction, varieties) testingInethods based on visible-near infrared hyperspecTral imaging[J]. OpLik, 2016, 127: 2624-2630[14] FAN S X, ZHANG B H,LI J B, et al. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data[J. Postharvest Biology and Technology, 2016, 121: 51-61[15 RAJKUMAR P, WANG N,EIMASRY G, et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[ JIJournal of Food Engineering 2012, 108: 194-200,2015,36(16):10172015,35(8:2297-2302[18]WANG N,2007,23(2:151-155.「192008,39(5):91-9320」201536(10:70-74.[21] WU D, SUN D WAdvanced applications of hyperspectral imaging technology for food quality and safety analysis and assess-ment a review part T[J]. Innovative Food Science and Emerging Technologies, 2013, 19(4): 1-14J2014,35(8:57-61BP,2012.124」13,44(2):142-146.25],201523(6:1530-1537M011:41-48.[27,2013,24(10:1972-19762010,30(10):2729-2733?1994-2018ChinaAcadcmicJournaleLcctronicPublishingHousc.Allrightsreservedhttp://www.cnki.nct
    2020-12-07下载
    积分:1
  • 直接序列扩频的SIMULINK仿真,含捕获与跟踪,解调
    直接序列扩频的SIMULINK仿真,含捕获与跟踪,解调,非常行
    2019-04-24下载
    积分:1
  • 696518资源总数
  • 105958会员总数
  • 18今日下载