contourlet_toobox

对话框以及树控件的描述及应用,很值得初学者学习，代码可以直接运行(Dialog boxes, and a description of the tree control and application, it is worth learning for beginners, the code can be run directly)

c# 对任意网页进行截图 抓取

基于NCC（归一化互相关）的图像配准算法(NCC (normalized cross-correlation), image registration algorithm)

一套完整的图像处理与识别的源码（全中文注释） 这是我们内部开发与使用的一套图像处理代码，里面包括了大部分图像处理与识别处理的的函数：像模板变换，图像的平滑、锐化、边缘识别、图像中值滤波、图像模糊、图像逆滤波、图像模糊加噪、图像维纳滤波、线性变换、灰度拉伸、直方图均衡、图像腐蚀、图像膨胀、图像开运算、GIF确码编码、缩放、旋转、快速付立叶变换、离散余弦变换等等，等等，太多了。可以用于windows软件或嵌入式软件编程。内部用的，全中文注释，为了下载我才拿出来的。你们自己看着办吧。(a complete set of image processing and recognition of the source code (all Chinese Note) This is our internal development and use of a image processing code, includes the most image processing and recognition processing function : as template transform, Image smoothing, sharpening, edge recognition, image filtering and fuzzy images, image inverse filtering, fuzzy image noise increases, Wiener filter images, linear transformation, gray tensile, histogram equalization, images corrosion, expansive images, image open operation, GIF correct coding, zooming, rotation, fast Fourier transform, discrete cosine transform, and so on and so forth, that is too many. Can be used for windows software or embedded software programming. Internal use, and the whole Chinese translations, in order to download )

WPF网络流量仪表盘

C# 以任意角度旋转图片实例

用MATLAB实现的darkchannel算法，实现图像去雾(Image Haze Removal Using Dark Channel)

改进的sift算法，称为surf，来实现图像的匹配，并显示。 (Improved sift algorithm, known as the surf, to achieve the image matching, and displayed.)

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)

此代码是通过matlab实现图像的平滑与分割！(This code complete a image smooth and segmentation！)