elleSR_0.2

3种不同的分水岭分割方法以及三种方法的比较(3 different methods of watershed segmentation and Comparison of Three Methods)

本程序采用了特征线法计算管道阀门关闭时的水锤(This program uses a water hammer characteristic line method to calculate the pipe when the valve is closed)

说明：  图像融合可以用的PCNN工具箱，可以使用在图像融合里(PCNN Toolbox for Image Fusion)

活动目标检测源码,用混合高斯算法编写，适用于复杂场景视频监控(alive target detection，used in GMM algorithm)

说明：  基于相邻像素间位异或的图像置乱算法,效果很好，改变图像直方图(Between the adjacent pixels based on the XOR bit of image scrambling algorithm, well, change the image histogram)

一种新的基于snake模型的水平集图像分割方法：用matlab编程实现，可进行灰度图像的分割，里面有演示实例，便于学习。(A new snake model based on the level set image segmentation methods: using matlab programming implementation, can be gray-scale image segmentation, there are demonstration examples for study.)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

画矩形的代码，包含各种算法，具体的自己看吧，这是图形处理的作业之一(Draw a rectangle of the code, contains a variety of algorithms, specifically for yourself, this is one of the graphics processing jobs)

建立一个透明物体上有污点的模型，可以模拟玻璃上有污点的图象(Rendering of clean transparent objects has been well studied in computer graphics. However, real-world transparent objects are seldom clean—their surfaces have a variety of contaminants such as dust, dirt, and lipids. These contaminants produce a number of complex volumetric scattering effects that must be taken into account when creating photorealistic renderings. In this paper, we take a step toward modeling and rendering these effects. We make the assumption that the contaminant is an optically thin layer and construct an analytic model following results in radiative transport theory and computer graphics. Moreover, the spatial textures created by the different types of contamination are also important in achieving visual realism. To this end, we measure the spatially varying thicknesses and the scattering parameters of a number of glass panes with various types of dust, dirt, and lipids. We also develop a simple interactive synthesis tool to create novel instances of the me)

奇异值分解波束形成，自适应波束形成仿真。由均匀线阵形式，确定阵列的导向矢量；由阵列导向矢量，对接收信号进行建模仿真；根据约束条件，实现自适应波束形成算法；考察算法性能与信噪比，采样率，观测时间等参数的关系。 (SVD beamforming, adaptive beamforming simulation. Form by the ULA to determine the array steering vector by the array steering vector, the received signal modeling and simulation under constraint conditions, adaptive beamforming algorithm examine algorithm performance and signal to noise ratio, sample rate, observation time, etc. parameters.)