Qt5实现无边框可移动可拉伸靠边自动分屏的自定义窗体

模糊神经网络matlab源程序，模糊+神经网络+遗传算法

本程序使用J2SE部分知识实现的毕业设计，附代码，论文，很全

基于红外图像低分辨率、低对比度、视觉特性差的特性，以及传统的利用直方图均衡化进行红外图像增强的方法会丢失图像的细节信息、增强红外图像的噪声的特性，将小波变换的多尺度、多分辨率的特点和直方图均衡化的方法相结合，提出一种更好的实现红外图像增强的算法。激光与红外No.22013尹士畅等基于小波变换和直方图均衡的红外图像增强227度下的原因,手臂的温度和环温比较接近,从而使得得到的图像的对比度比较差,视觉效果不明显。如图2中为经过小波变换后提取出来的图像的低频成分,从中可以看出,该图像和原始图像的对比度差别不大,但是从视觉上来看,图片的连续性较好,噪声较少。图3是经过直方图均衡化处理的图像,经过直方图均衡化之后图像的整体的视觉效果变好了,图片中手表和手臂的对比度非常明显,甚至包括表图4本文增强算法带和手臂的也可以清楚地辨认出来。然而,经过直7结论方图均衡化之后,手臂左下角方向和右下角方向以针对直方图均衡化和小波变换在红外图像增强及手表中央的噪声也变得非常的大,相比较原始图存在的问题,本文所提出的改进算法,通过将两者的像而言信噪比变差了。图4则是将直方图均衡化和优势相结合,弥补单独算法的劣势,从而达到适当提小波变换算法相结合后增强的红外图像,相比较图高原始红外图像的对比度,增强了目标和背景的差3而言,对比度的变化不大,但是图像的很多噪声特异性并且保证红外图像的信噪比的效果。性得到了改善,尤其是手表中央和手臂的左右下角部分的噪声得到了明显改善,从而很好的验证了该参考文献:算法的可行性。[1 Lin Zhenxian Song Guoxiang, Xue Wen Comparison andimprovements of several methods wavelet image denoising[ J]. Journal of Xidian University, 2004, 31(4)625-629.( in Chinese)林椹尠,宋国乡,薛文.图像的几种小波去噪方法的比较和改进[J].西安电子科技大学学报,2004,31(4):625-6292 Yu Tianhe, Hao Fuchun, Kang Weimin Summarization onthe infrared image enhancement technology [J]. Infrared图1原始红外图像and Laser Engineering, 2007, S2): 131-137.( in Chinese于天河,郝富春,康为民红外图像增强技术综述[J]红外与激光工程,2007,(S2):131-137[3 Xie Jiecheng, Zhang Dali, Xu Wenli. Wavelet Image De-noising vigorously [ J]. Journal of Image and Graphics2002,7(3):209-218.( in Chinese)谢杰成,张大力,徐文立小波图象去噪综述[J].中国图象图形学报,2002,7(3):209-218图2低频红外图像[4 Peng Zhou, Zhao Baojun. Nover scheme for infrared imageenhancement based on contourlet transform and fuzzy theory[J]. Laser& nfrared,2011,41(6):129-133.彭洲,赵保军.基于 Contourlet变换和模糊理论的红外图像增强算法[J].激光与红外,2011,41(6):129-133[5 Yong Yang, Wang Jingru, Zhang Qiheng. Enhancement oflow Contrast Image Contain Small Targ[ J]. Laser &Infrared,2005,35(5):373-377.( in Chinese)图3直方图均衡化雍杨,王敬儒,张启衡.弱小目标低对比度图像增强算228激光与红外第43卷法研究[J].激光与红外,205,35(5):373-377round[ J. Laser Infrared, 2003, 33(6): 109-114.[6 An Chengbin, Ren Hongliang, Nei Chuanhong, et al. Infraincsered Image Enhancement Technology for Staring Infrared温佩芝,史泽林,于海斌基于小波变换的复杂海面背Imager[ J]. Laser Infrared, 2003, 33(6): 32-33. (in景红外小目标检测[J]激光与红外,2003,33(6)nese109-114安成斌任宏亮,传虹,等凝视焦平面热像仪的红[11]孙延奎小波分析及其应用M].北京:机械工业出版外图像增强技术[J].激光与红外,203,33(6):社,2005[12] Turghunjan, et al. a technique of image enhancement[7]宋芳莉图像边缘检测中的方法研究[D].西安:西北based on the dyadic wavelet transform[ J]. Joumal of Xin-大学,2002jiang Normal University Natural Science Edition, 2006[8 Luo Jiebo, Chen Changwen, Parker K J Image enhancement25(4):6-13for low bit rate wavelet-based compression[ J]. IEEE Inter吐尔洪江,等.基于二进小波变换的图像增强技术national Symposium on Circuits and Systems, 1997: 6-20[J].新疆师范大学学报:自然科学版,2006,25(4)[9 Ji Shupeng, Ding Xiaoqing. Study on image enhancing fusion algorithm of visible and infrared image[J]. Laser [13]S Mallat. a Wavelet Tour of Signal Processing[ M].PittsInfrared, 2002, 31(6): 518-521.( in Chinese): Academic Press, 1999.吉书鹏,丁晓青.可见光与红外图像增强融合算法矸4]张德丰 MATLAB小波分析[M].机械工业出版社究[J激光与红外,2002,31(6):518-5212009[10] Wen peizhi, Shi Zhelin, Yu haibin. Wavelet transform-[15]葛哲学,沙威.小波分析理论与 MATLAB R007实现based Detection for Small IR Target in Complex Sea Back-[M].北京:电子工业出版社,2007

本课程设计主要介绍通过进行QPSK调制解调的基带仿真，对实现中影响该系统性能的几个重要问题进行了研究。针对QPSK的特点，调制前后发生的变化，加上噪声后波形出现的各种变化，通过星座图、眼图、波形图等来观察。程序设计与仿真均采用MATLAB集成环境下的Simulink仿真平台，最后仿真详单与理论分析一致。   

图像分割算法，区域生长法，基于二个种子点分割脑部MRI图像，程序完整，希望跟大家分享。

本资源是本人毕业设计部分代码，已通过Cyclone II开发板验证，实现了HDB3码的编码及译码功能

esprit算法提供了DOA估计的几乎所有算法的matlab程序,有注释,而且对多种算法进行了比较,图像清晰

自己正在使用的c语言文档，比较全面

Hadoop课程实验和报告——Apriori算法并行实现

站里这方面资源不多，特别是种子区域生长方面的很少，上传一个算一个吧rightnumleftnuIm percent2()Ivaluerightnumipercent2(rvalue)pm= lvalue/‖P‖,pma= rvalue/‖P‖‖Pwindows xpMATLAB (R2008aOtsa.RlenI=ll, Rlen2=60, percent1=55%c, percent270%b. Rlenl=ll, Rlen2=80, percent1=50%, percent2=94%c.Rlen1-1l, Rlen2-100 percent1-65%. percent2-80%d. Rlen1-1l, Rlen2-=80, percent1=60%. percent2-83%2cent 1percent2Percentppercent 13C1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net[J]2007,29(6):854-857[J]26(2):93-962010,45(7):76-80[JI.1963:1l5-12005,26(11):[J],2005,17(1):1-3Research on the improved algorithm and application ofseed region growth method and its sail image extrationYAN Shen-hai, HUANG Xian-tong, LIU Yang(School of Mathematics Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, ChinaAbstract: Seeded region growing is a common method of image segmentation. Its performance depends largelyon the seleclion of seed points and growth rules. In order Lo extract snail images with such methods more effectively.The article analyzes the basic idea of the seeded region growing method(SRG) and presents an improved seededregion growing method (ISRG) which is used to extract the snail images. In ISRG, novel similarities rules and anew dynamic threshold method are adopted. For the complex and various habitat of snail, ISRG selects the region growing seed points manually. It is demonstrated by experimental results that ISRG can achieve better snailimage extraction results under the complicated real-world sceneKey words: seeded region growing (SRG; image extraction; dynamic threshold; snail(E D:X, JC1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net