image-segmentation

利用大津算法对图片进行处理，得到满意的处理效果。(The image is processed by Otsu algorithm and satisfactory results are obtained.)

基于CDD模型的快速数字图像修复算法 matlab版 (CDD model based on fast digital image restoration algorithm matlab version)

双目匹配计算视差，生成深度图。（前提是用自己标定好的摄像头拍的图片，导入内外参数然后进行立体匹配）(Binocular matching calculates parallax and generates depth map.(The premise is to take a picture with your own calibrated camera, import internal and external parameters, and then stereo matching))

此程序为计算雷达目标特性角闪烁特性的函数代码(computing the anglar glint)

针对遥感图像可控参数误差造成的图像模糊，进行模糊核估计(Remote sensing images for image blur caused by controllable parameter error, blur kernel estimation)

编程实现灰度图像的几种常用的边缘检测算法，包括：梯度边缘检测算法、Roberts边缘检测算法、Sobel边缘检测算法、拉普拉斯边缘检测算法、canny边缘检测算法、Prewitt边缘检测算法和Krisch边缘检测算法。

论解读DICOM医学图像文件及其显示的VC++实施方法 是一份比较新的讨论dicon的vc实现方法的文章(read DICOM medical images and documents show that the implementation of the VC is a relatively new discussion di vc con method of the article)

对二维码图像进行灰度化、中值滤波、二值化、边缘检测、霍夫变换等预处理(The two-dimensional code image is grayscale, median filter, two valued, edge detection, Hough transform and other pre-processing.)

c++语言或MATLAB编程对256×256的灰度图像进行8×8DCT变换，对变换后的结果进行处理： 采用区域编码和阈值编码方法实现图像压缩，保留 50％ 的系数(区域编码保留前 50%个系数，阈值编码保留 50%的大系数）； 再反变换后，对比原始图像和处理后的图像。(C++ or MATLAB programming performs 8 * 8DCT transform on 256 * 256 grayscale images, processing the transformed results: The region coding and threshold coding are used to compress the image, and the coefficient of 50% is retained (the first 50% coefficients are reserved for the region code and the large coefficient of the threshold code is retained by 50%). After inverse transformation, the original image and the processed image are compared.)

采用分水岭算法对图像进行分割。直接对图像进行分水岭算法，效果并不理想。本方法使用形态学重建技术对前景对象进行标记，求局部极大值，最终将分割好的图像进行伪彩色显示，让结果更加明显。适用于较复杂的图像分割处理。(Using watershed algorithm for image segmentation. Watershed algorithm for image directly, the effect is not ideal. This method uses morphological reconstruction technique foreground object is marked, find local maxima, eventually dividing good pseudo-color image display, make the results more apparent. Applicable to more complex image segmentation processing.)