jbigkit-2.1

说明：  本文简要回顾了OpenCV的发展，介绍了OpenCV的各种应用，并详细分析了其中一些应用的原理、算法和代码，最后还介绍了一些关于OpenCV的最新研究进展。(This paper reviews the development of OpenCV, OpenCV introduces a variety of applications, and application of a detailed analysis of some of the principles, algorithms and code, and finally describes some of the latest research on the progress of OpenCV.)

数字图像处理(matlab版) 自带的 DIPUM_toolbox, v1.1.4，m文件(dipum toolbox v1.1.4 m files)

说明：  matlab软件进行近红外光谱图像处理mwpls方法，进行最佳光谱区间的选择(Matlab software for near infrared spectral image processing mwpls method, the best spectral range selection)

能同时显示24位图三色R、G、B的直方图(can also showed 24 Figure 3 color R, G, B Histogram)

小波变换方法采用对高频系数平均的方法实现图像融合算法(Wavelet transform coefficients using the average of the high-frequency method to achieve image fusion algorithm)

实现模糊聚类算法（FCM）与马尔科夫随机场空间约束（MRF）的图像分割、SAR图像变化检测(Fuzzy Cluster Method and Markov Random Field for Image Segmentation or SAR Change Detection)

这是一个关于振幅匹配的程序，C环境开发，可用于地震资料振幅匹配(This is a very useful program for amplitude matching by C)

能够实现对高光谱数据影像的平滑，可以根据需要设置平滑参数并且能够批量处理(Hyperspectral Smooth)

电压跌落是最严重的动态电能质量问题之一， 精确定位电压跌落起止时间是应对电压跌落问题的 重要前提和基础。由于电压采样信号往往有噪声分 量，现有的方法在定位电压跌落的起止时间时存在 局限性。本文提出利用多小波变换及相邻系数去噪 的电压跌落定位方法。多小波兼有对称性、正交性、 有限支撑性和二阶消失矩等优异的信号处理性能， 利用GHM多小波可以准确定位电压跌落起止时间。 多小波变换系数在每层之间具有对应关系，多小波 相邻系数将紧相邻的若干个系数作为一个整体来确 定阈值，考虑了系数之间的相关性，能获得更好的 去噪效果。通过 Matlab 进行仿真验证，仿真结果表 明，所提出的方法的正确性。 (Voltage sag is one of the most serious dynamic power quality problems. Critical start-time and end-time are important indices for voltage sags. But the sampling signals often have noisy component, the locations of start-time and end-time are hard to get. Wavelet is an effective tool for those non-stationary signal processing and has been used in this field. Local feature in the signal can be enlarged after the transformation using the scalar wavelet. But scalar wavelets cannot contain orthogonality, symmetry, compact support and higher order of vanishing moments simultaneously. In this thesis, multi-wavelets GHM is used to detect and locate power quality disturbances. Multi-wavelets offer many excellent properties such as the same approximation order but more compact support. The dependence of the multi-wavelets coefficients varies with the level, so neighboring coefficien)

基于混合高斯的运动目标检测程序已经相应的文章！非常值得借鉴！(Gaussian mixture-based moving object detection procedures have been the appropriate article! Well worth learning!)