shuangyuzhi

说明：  对火焰图像的特征进行提取，如火焰闪烁频率、火焰区域面积，火焰燃烧区域裁剪等( the Feature Extraction of flame )

可以对边缘检测出来的椭圆,实现椭圆参数的自动提取,非常的好用,自己编写的,请各位大哥多指教,QQ:526289(be on the verge of testing the ellipse, the ellipse parameters to achieve the automatic extraction, very convenient, their preparation, please enlighten you more than big brother, QQ : 526289)

图像处理,图像去雨滴、尘土，达到图像恢复的效果，还原图像。(image derain)

保铮院士的《雷达成像技术》一书，对雷达成像原理等方面做了较详的介绍，对学习微波方面的有借鉴。(Bao Zheng academician of the radar imaging technology, a book on the radar imaging principle, etc. have done a more detailed description of the study area are from the microwave.)

该算法是局部线性嵌入的算法，代码无误，已运行，并且附带数据集，可以使用，非常实用。(The algorithm is locally linear embedding algorithm, the code is correct, has been running, and with the data set, you can use, very practical.)

实现峰值信噪比的算法，用于判断图片质量的好坏！十分有用！(Achieve peak signal-to-noise ratio (PSNR) algorithm for judging image quality! Very useful!)

log Gabor小波图像纹理特征分析我小改過(log Gabor wavelet texture features of image I have little turn over)

针对空间发光点缩成光斑图像中心的高精度提取问题，本文提出一种亚像素提取方法。该方法简历了具有高斯能量分布的空间发光点的透视投影光斑图像灰度分部数学模型，证明了空间发光点能量中心的透视投影不变性。(Light-emitting points of space shrunk to spot high-precision center of the image extraction problem, we propose a subpixel extraction method. This method resumes a space with Gaussian energy distribution points of perspective projection spot light gray segment mathematical model to prove the energy center of the space light-emitting point perspective projection invariance.)

视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)

实现了图像的加水印，去噪，加噪声等功能，基于kaiser窗的双谱线插值FFT谐波分析，用于建立主成分分析模型。( Realize image watermarking, de-noising, plus noise and other functions, Dual-line interpolation FFT harmonic analysis kaiser windows, Principal component analysis model for establishing.)