iris_gabor_filter

说明：  图像凭借代码，捕获摄像头进行图像拼接。利用柱面投影的方式进行投影，然后对图像进行拼接。(image stitching program)

USB Composit example for HID

New Tracking Algorithm for Wireless network

计算三维数据的法矢量并进行调整，里面包括了自己做的实验数据和结果(Three-dimensional data, vector calculation method and make adjustments, which includes the make their own experimental data and results)

说明：  VC++6.0图像处理算法集:图象的几何变换、平滑(去噪声)、锐化、腐蚀、膨胀、细化算法、半影调、抖动、直方图修正、彩色变换、腐蚀，膨胀，细化等算法，并有可执行文件供试用，并附有配套电子收，是图像处理入门的好资料。(VC++ 6.0 image processing algorithms: image geometric transformation, smoothing (to noise), sharpening, corrosion, expansion, thinning algorithm, penumbral tune jitter histogram of the amendment, color conversion, corrosion, swelling, refinement algorithm, etc., as well as executable files for trial, together with supporting electronic collection, image processing is a good data entry.)

用matlab编写的图像恢复程序，实现压缩后图像的恢复()

java 图形算法实现源代码，有坐标放大缩小。(java graphics algorithm source code, coordinates Zooming.)

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)

Isoperimetric Graph Partitioning for Image Segmentation

transver mat file format to raw file format, simply matlab code, usd to 3D medical image processing/data input