gmm-cv

使用二维经验模态分解进行图像的去噪操作，内含三幅测试图片和说明文档(Using Bi-dimensional Empirical Mode Decomposition for image denoising operation)

说明：  1.学习使用fft2,ifft2,fftshift,ifftshift函数。用fft2函数对”lena.bmp”做二维傅立叶变换，并显示其频谱，用fftshift函数将频谱坐标原点移动到图像中心。用ifft2函数做傅立叶反变换。（提示：显示频谱时，可以先对频谱取对数，这样可以压缩动态范围很大的频谱，获得更好的显示效果）(1. Learn to use fft2, ifft2, fftshift, ifftshift functions. With the FFT 2 function, the lena.bmp'The two-dimensional Fourier transform is performed and the spectrum is displayed. The origin of the spectrum coordinate is moved to the center of the image by using the FFT shift function. The inverse Fourier transform is done with ifft2 function. (Note: when displaying the spectrum, the logarithm of the spectrum can be taken first, so that the spectrum with a large dynamic range can be compressed to obtain better display effect.))

根据厄米高斯光束的振幅分布物理公式来画厄米高斯光束的强度分布图(plot the intensity distribution of the HermiteGaussian Beams based on the physical formulars of the Hermite-Gaussian Beams )

几个大牛对集成了国内外特征提取最前沿的算法，特征提取，DOG,HARRIS,HARRIS-LAPLACE,HARRIS-AFFINE,MSER.描述符，SIFT,AFFINE-SIFT,GLOH.经测试，速度快，描述符准确。(Integration of feature extraction algorithms from several expert of domestic and international, feature extraction： DOG, HARRIS, HARRIS-LAPLACE, HARRIS-AFFINE, MSER； Descriptors： SIFT, AFFINE-SIFT, GLOH. The test reveal the speed and accurate descriptor.)

图像处理中的基本问题 做对比的试验(Image processing compared to the basic problems of the pilot)

混合高斯分布EM算法，可以算混合高斯分布的三个参数。混合高斯分布更接近系数分布。(mixed Gaussian distribution EM algorithm can calculate mixed Gaussian distribution of the three parameters. Mixed closer to the Gaussian distribution coefficient.)

利用小波变化实现对不同灰度图像的边缘检测(Changes implemented using wavelet different gray-scale image edge detection)

给予稀疏编码的原理将10幅自然图像进行系数分解成为图像基。(Given to the principle of sparse coding will be 10 coefficients of natural images into separate image-based.)

说明：  图像去噪，降低图像的噪声，提高图像PSNR(image denoise,improve the psnr of image)

图着色问题（Graph Coloring Problem, GCP）又称着色问题，是最著名的NP-完全问题之一。路线着色问题是图论中最著名的猜想之一。(Graph coloring problem (Graph Coloring Problem, GCP), also known as coloring problem, is the most famous NP-complete problems. Line graph coloring problem is one of the most famous conjecture.)