数字图象处理

人体脑图像分割技术研究a. 对原始的二维医学图像进行滤波，封装成三维体数据。 b. 采用合适的多阈值分割方法得到合适的双阈值，对三维体数据进行二值处理，得到分割出的三维人脑图像。 c. 对得到的三维人脑二值图像进行数学形态学操作，并利用种子填充进行区域修整，得到较好的三维人脑分割结果，并给以三维显示。 (err)

实现了添加水印及攻击水印的一些方法。攻击水印方法包括抖动攻击、滤波攻击、旋转攻击等(Add watermark and implementation of a number of methods to attack the watermark. Watermarking attack methods include jitter attacks, filtering attacks, spin attack)

The QTimer class provides repetitive and single-shot timers for Linux.

自动图像拼接，利用SIFT特征匹配算法，RANSAC(random sample consensus)算法，加权平滑算法等技术克服了传统图像拼接技术中的局限性(如光照、尺度变化的影响等)，实现了光照和尺度变化条件下的多视角无缝图像拼接(Automatic Image Stitching  Local features detection  Features Matching  Homography Estimation RANSAC Image blending )

文献，基于融合和聚类的遥感图像变化检测技术(Literature, based on remote sensing image fusion and change detection clustering technology)

【实例简介】

一个功能强大音频重采样算法，供大家学习参考(A powerful audio resampling algorithm for learning the reference U.S.)

说明：  用最大相似性方法实现独立分量分析算法的matlab代码，可用于盲信号处理和图像滤波器构造。(with the greatest similarity method independent component analysis algorithm Matlab code can be used to blind signal processing and image filter structure.)

激光标定中得激光线检测程序，激光标定中得激光线检测程序(A laser line detection program was the laser line detection procedures, laser calibration laser calibration)

转：https://blog.csdn.net/jinshengtao/article/details/26278725 混合高斯背景建模是基于像素样本统计信息的背景表示方法，利用像素在较长时间内大量样本值的概率密度等统计信息(如模式数量、每个模式的均值和标准差)表示背景，然后使用统计差分(如3σ原则)进行目标像素判断，可以对复杂动态背景进行建模，计算量较大。 在混合高斯背景模型中，认为像素之间的颜色信息互不相关，对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点，其值在序列图像中的变化可看作是不断产生像素值的随机过程，即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰)，多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak).)