FreeImage380Chinese

局部投影去噪 自己编的 调试已经成功 拿来给大家借鉴 对象是混沌信号(Local projective denoising their own series of debugging has been successfully brought to everybody' s reference object is a chaotic signal)

基于ransac的椭圆检测，对于一组随机的椭圆点，可以得到椭圆参数，检测精度高。(Based ransac ellipse detection, for a group of random elliptic points, you can get an ellipse parameters, high detection accuracy.)

haar小波变换 在图像配准中可以使用 进而进行基于小波金字塔的模板交互匹配(haar wavelet transform in image registration can be used to carry out based on wavelet pyramid interactive template matching)

ct扇束等角滤波反投影重建算法matlab源码 分成源图像创建 投影 滤波反投影三个文件(ct Matlab reconstruction algorithm which can be used for CT image reconstruction)

扫描文件夹，通过文件大小和创建时间判断图片文件是否重复，并分类和重命名(Scanning folders to determine whether image files are duplicated, categorized and renamed by file size and creation time)

matlab运动目标检测代码，应用了高斯混合滤波。(something useful for matlab)

能比较全面的图像处理系统。主要内容包括：位图基础、图像的显示、图像的几何变换、图像灰度变换、图像的平滑处理、图像锐化处理及边缘检测、图像分割及测量、图像的形态学处理、图像的变换域处理及应用、图像的合成、24位彩色图像的处理、JPEG图像的压缩编码(can more comprehensive image processing system. Main contents include : bitmap based, image display, image geometry transformation, gray transform, image smoothing, image sharpening and edge detection, image segmentation and measurement, image morphology, Image Transform and application processing, image synthesis, 24-bit color image processing, JPEG image compression coding)

ROA算法代码，用来进行SAR图像的道路段检测的比较流行的算法之一(for SAR road detection)

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)

用matlab实现的霍夫曼编码的图像压缩，主要就是依据霍夫曼编码的原理实现的图形图像处理。(Using the Huffman code matlab realize the image compression, Huffman coding is mainly based on the principle realize graphics image processing.)