Gamma_MAP

meanshift算法的车辆跟踪，效果非常好，改进版的，希望对大家有用(Meanshift algorithm of vehicle tracking, the effect is very good, improved version of the hope of the useful)

为数字图像水印提供多种攻击方式，其中包括JPEG压缩、高斯低通滤波等多达十种攻击方式。(Offers a variety of attacks of digital watermarking ,including the jpeg compression, Gaussian low-pass filtering, up to ten different attacks)

2016华为软件精英挑战赛，采用迪杰斯特拉算法，效率高，但需要自行修改(HUAWEI software challenge 2016)

对图像进行伽马校正，可以有效提升图像 亮度和对比度，低于低照度图像现在小于1的参数，对于高亮度的图像选择大于1的参数可以有效调节图像亮度。(Gamma correction can effectively improve the brightness and contrast of the image. The parameters lower than the low illumination image are less than 1. For the high brightness image, the selection of parameters greater than 1 can effectively adjust the brightness of the image.)

说明：  利用matlab对只包含RBG三个波段的图像进行植被提取，通过设定一定的阈值将植被与背景进行分割(The vegetation is extracted from the image containing only three bands of rbg by matlab, and the vegetation and background are segmented by setting a certain threshold.)

输入一组数据，xy值，用来拟合椭圆。只要改变输入数据，该代码就可以直接使用。另附上椭圆拟合算法。(Enter a set of data, xy values, used to fit ellipses. Just change input data, the code can be used directly. Attached on the ellipse fitting algorithm.)

说明：  通过利用“Otsu”方法对图像进行全局阈值分割处理。然后，将灰度图转化成二值图，为获得大豆颗粒计数和大小做准备。调用函数获得图像连通图的数量，并测量标注矩阵中每个标注区域的属性，获得每个标注区域的长度。最后通过寻找每个区域的重心数，即可统计出大豆的个数(The Otsu method is used to segment the image. Then, the gray-scale image is transformed into a binary image to prepare for the soybean particle count and size. Call the function to get the number of connected graphs, measure the attributes of each annotation region in the annotation matrix, and get the length of each annotation region. Finally, the number of soybean can be calculated by finding the number of barycenters in each region)

数字图像处理实验-图像处理-含实验题目及源代码(Pseudo-Color Image Processing (a) Implement Fig. 6.23, with the characteristic that you can specify two ranges of gray-level values for the input image and your program will output an RGB image whose pixels have a specified color corresponding to one range of gray levels in the input image, and the remaining pixels in the RGB image have the same shade of gray as they had in the input image. You can limit the input colors to all the colors in Fig. 6.4(a). (b) Download the image in Fig. 1.10(4) and process it with your program so that the river appears yellow and the rest of the pixels are the same shades of gray as in the input image. It is acceptable to have isolated specs in the image that also appear yellow, but these should be kept as few as possible by proper choice of the two gray-level bands that you input into your program.)

用于图像去噪处理Tikhonov模型，仅供用于学习参考(Tikhonov model for image denoising)

Image segmentation systems have the potential to dramatically improve the performance of Digital image processing. Segmentation procedures partition an image into its constituent parts or objects. In general, segmentation is one of the most difficult tasks in digital image processing. Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. This problem can be tackled by mapping the pixels into various feature spaces, which are subjected to various grouping algorithms It present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.