Matlab_MRI_3Dreconstruction

实现人脸识别功能，GUI界面，其中有测试人脸集，识别率95以上(To achieve face recognition function, GUI interface)

KALMAN filtering algorithm under the matlab toolbox, Shared by everybody, good source is very good

画任意旋转点源码，例程程序使用画板，根据两点的坐标及旋转角度画出旋转点的坐标。(Videos arbitrary rotation point source , a routine procedure using Sketchpad , coordinate rotation point to draw the coordinates of two points and the angle of rotation .)

一种图像对比度增强算法，包含demo，和图像性能测试(An image contrast enhancement algorithms, including demo)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)

说明：  水下图像融合，图像融合，matlab语言(Underwater image fusion, image fusion, MATLAB language)

说明：  360°图像序列的柱面全景拼接算法，matlab仿真程序。(360 image sequence cylindrical panoramic mosaic algorithm, matlab simulation program.)

实现图像感兴趣区域的选取，代码完整，可直接调用，具有一定的参考价值(Image of the selected region of interest, code integrity, and can be directly called, has a certain reference value)

说明：  有数字1和2的图片各四十张，利用mat lab读取图片并将其二值化，得到只由0和1表示的矩阵，根据数字的不同特征，选择两个特征值，用来对分类器进行训练，最后用剩下的5个1和5个2来进行测试，看分类器是否可以正确的对数据进行分类。(Figures 1 and 2 pictures of the 40, using mat lab picture and secondly, the value of reading, and received only by 0 and 1, said matrix, according to the number of different features, select the two characteristic values, used to classifier training, and finally with the remaining 5 1 and 5 2 to carry out tests to see whether the classifier can correctly classify the data.)

将一幅M×N的灰度图像用3×3平均滤波器进行模糊，分别再加上一定的高斯 噪声和均匀噪声。然后，设计一个维纳滤波器对这两幅图像进行复原，分别 计算这两幅图像复原前后的PSNR。(Gray-scale image of a MN 33 average filter is vague, respectively, plus a Gaussian noise and uniform noise. Then, design a Wiener filter to recover these two images, the two images before and after the recovery of PSNR were calculated.)