myenviread

带高斯白噪声的Kalman滤波Matlab代码，每一步都有详细的注释，和kalman滤波的五个公式对应。并有高斯白噪声的产生函数，function G = CreateGauss(E,D,M,N) 产生均值为E,方差为D,MxN的高斯白噪声矩阵(The Matlab code of Kalman filter with Gaussian white noise.)

带色彩恢复因子的retinex算法，用于处理雾天图像(Recovery factor with color retinex algorithm, used to deal with fog image)

按照夹角余弦距离计算待测样品与样品库的样品相似度(Cosine distance calculation in accordance with the test sample and the sample database sample similarity)

主动轮廓模型也就是snake模型进行图像分割，一种由图像高层信息的图像分割算法。(snake model，active contour model，image segmentation)

精心收集的几个去噪算法，包括基于视觉的非线性去噪、BLS_GSM算法、NL-Bayes图像去噪、KSVD去噪等，内容为核心代码，需要配置完善。 (Denoising algorithm of several carefully collected, including visual based nonlinear denoising, BLS_GSM algorithm, NL-Bayes image denoising, KSVD denoising, the content as the core code, the need to improve the allocation of. )

经典的针对SAR图像边缘检测的ROA算法(edge detection using ROA operator in SAR image)

源码包括了常用的图像处理方法，代码详细，适合初学者(Source code, including the commonly used image processing method, the code in detail, suitable for beginners)

说明：  matlab实现视频中动态目标跟踪，跟踪动态目标，确实效果很好(matlab implementation of dynamic video target tracking, tracking dynamic target is indeed good results)

说明：  文献 "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" 的参考代码 用Lp正则化方法做盲复原的代码 demo_text_deblurring 是主函数(refer to "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" main function: demo_text_deblurring)

实现一个高精度指针仪表读数自动识别系统．用数码摄像头采集仪表的图像，然后利用数字图像处理技术识别刻度和指针，根据指针和刻度的相对位置计算指针读数值．使用条件霍夫变换（Constrained HOUGH Transfer）和中心投影分析法实现刻度的全自动识别，采用快速中心投影法进行指针检测与识别，识别速度达到68ms．实验结果表明该方法比传统方法速度快、精度高，系统读数平均误差仅为0．016％(To achieve a high precision automatic identification system pointer meter reading. Instrument with a digital camera capture images, then using digital image processing technology to identify scale and pointer, pointer and scale according to the relative position of the pointer readings calculated. Use Hough transform (Constrained HOUGH Transfer) and center projection analysis method to achieve scale automatic identification method using fast center projection target detection and recognition, recognition speed of 68ms. Experimental results show that faster than traditional methods, high accuracy, the system average error of only 0.016 of reading)