PDE_Programs

the blink eyes tracking

一些常用的图像处理评价指标，处理后图像对比度，边缘剩余数，处理前后图像的相似性....(Some commonly used image processing evaluation, contrast, edges remaining number of similarities ....)

强化细节的自适应直方图均衡化,分块的算法，可以自动调节窗口大小进行局部增强。()

这是一个matlab的代码。利用自带函数，代码实现了医学图像的配准(This is a matlab source. Code implements images registration)

说明：  利用BP神经网络进行图像分割。主要适用于RGB信息丰富的图像。以RGB为BP网络的三个输入，与对应的灰度图对网络进行训练。(The use of BP neural network image segmentation. RGB is mainly applied to information-rich images. BP network to RGB for the three inputs, with grayscale corresponds to the network training.)

说明：  利用97小波变换进行图像压缩，用matlab仿真，实验证明它具有较高的压缩比(using wavelet transform image compression using Matlab simulation experiment to prove that it has a higher compression ratio)

说明：  图像融合可以用的PCNN工具箱，可以使用在图像融合里(PCNN Toolbox for Image Fusion)

FDK算法 重建三维图像，期中有GPU并行计算加速fdk算法的实例(back-projection to RECONSTRUCT 3D IMAGE)

说明：  该课题为基于MATLAB平台的PCA的人脸识别图像考勤系统。传统的人脸识别都是直接人头和人头的对比，现实意义不大，这块都做烂了，不易通过毕设答辩，而且论文不好写。该课题识别原理为：从一副生活照中寻找到人脸，并且分割人脸图象，利用PCA算法进行降维，和库里图片进行对比，输出目标人脸以及记录识别的时间，可以统计人脸打卡的当月次数，从而统计出勤率。该课题还可以继续二次开发，做成库内外人脸的识别，如果是库外人脸则实现报警。GUI界面有清除数据功能，统计工资等功能。(This project is based on the MATLAB platform PCA face recognition image attendance system. The traditional face recognition is the comparison of direct head and head, which is of little practical significance. This part is not easy to pass the final defense, and the paper is not easy to write. The recognition principle of this subject is: find the face from a living picture, segment the face image, reduce the dimension by PCA algorithm, compare with the library picture, output the target face and record the recognition time, and then count the number of times in the month when the face is punched, so as to count the attendance rate. The subject can also be further developed to make face recognition inside and outside the database. If it is outside the database, the alarm can be realized. The GUI interface has the functions of data clearing and salary statistics.)

仿射变换来实现旋转 (线性变换)，平移 (向量加)，缩放操作 (线性变换)(Affine transformations are implemented by rotation (linear transformation), translation (vector addition), scaling operation (linear transformation))