ImageFusion

使用hough变换检测图像中的线条；包括main函数和其他4个操作函数，有实验图片！(Detection using hough transform image lines including the main function and operation of the other four functions, there are experimental picture!)

高光谱遥感图像光谱解混的独立成分分析技术(Hyperspectral remote sensing image spectra unmixing independent component analysis)

一个去除斑点噪声的滤波器的，特别适用于医学超声图像(A filter to remove speckle noise , especially for medical ultrasound image )

曲波变换程序代码，实现图像的曲波分解以及显示，可以去图像进行曲波去噪增强，对彩色图像空间模型转化后提取曲波信息(Qu Bo transform program code, and display the image decomposition Qu Bo, Qu Bo can go to image edge enhancement, color image space model for transforming information extracted after Qu Bo)

图像融合质量评价指标，包含灰度值，平均梯度，光谱扭曲程度，SSIM，PSNR，MSE等等。(Image fusion quality uation, including gray value, average gradient, spectral distortion degree, SSIM, PSNR, MSE, and so on.)

主要是关于模糊熵的，里面还和灰色关联度有点联系。搞图像的应该可以看的懂的。(Mainly on fuzzy entropy, which has little contact and the gray. Should be able to read out the image of understand.)

一个时钟的简单实例。背景为白色，可根据实际需要改表盘等。(A simple example of a clock. The background is white, can be changed according to the actual needs of the dial.)

一个基于MFC窗口的具有图形图像处理的功能的MFC程序，可对图像灰度，滤波等处理好功能(MFC program has a function of image processing based on MFC window can be a good feature for grayscale images, filtering and other processing)

快速角点检测算法，本算法针对图像中存在的线和点进行检测，算法通过matlab程序实现，结果表明具有很好的效果(Fast corner detection algorithm, the algorithm for images that exist in the line and point detection algorithm matlab program, results show that with good results)

（1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 （2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 （3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量； 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)