fitellipse

利用K均值聚类的方法实现人体的检测，混合高斯模型建立背景并实时更新(The use of K-means clustering method to achieve the human body detection, Gaussian mixture model to establish the background and real-time updates)

最大熵多阈值实现多红外图像的实时处理，多阈值实现(The maximum entropy multi-threshold to achieve real-time processing of multiple infrared image.)

图像去噪，采用GUI界面形式，里面包括高斯滤波，低通滤波和理想滤波三种方法。(Image denoising, using GUI interface form, which includes Gaussian filtering, low-pass filtering and ideal filtering three methods.)

基于VC环境下的直线裁剪算法Cohen-Sutherland算法和Liang-Barsky算法的实现(Based on the realization of linear cutting algorithm VC environment under the Cohen-Sutherland algorithm and Liang-Barsky algorithm)

利用ENVI/IDL，计算多幅图像的各个像元的平均值。并忽略空缺值(Use ENVI/IDL, calculate the average of each pixel of multiple images. And ignores the value of vacancies)

该程序主要利用快速傅里叶算法将振动信号的时域转化成频域信息，得出频谱进行分析。(The program mainly using fast Fourier algorithm time domain vibration signal converted into the frequency domain information, obtain spectral analysis.)

说明：  国外编写的双树复小波的一个开发包，包括1维和2维的情况，文件中有说明。(The Dual-Tree Complex Wavelet prepared outside of a development package, including the one-dimensional and two-dimensional case, the document has made it clear.)

BP神经网络，可以处理图像进行分类等各种处理的源码(BP neural network image processing source code)

说明：  使用matlab编写的源程序 高斯噪声产生的源代码(prepared by the use of Matlab source Gaussian noise source)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)