Hilbert-transform

说明：  图像半监督聚类算法flicm算法的matlab，内有图像数据集，可直接使用(Image semi supervised clustering algorithm flicm algorithm matlab, there are image data sets, can be used directly)

bgslibrary_x86_v1.9.2,是MFC的实现文件，包含37种背景减除算法，对于做目标提取的研究非常有用。(The BGSLibrary was developed by Andrews Sobral and provides an easy-to-use C++ framework based on OpenCV to perform background subtraction (BGS) in videos. )

说明：  通过检测二维小波变换的模极大值点可以确定图像的边缘点，利用小波变换的多尺度性用于图像的边缘检测(By detecting modulus maxima point of two-dimensional wavelet transform we can determine the image edge points, using multi-scale nature of wavelet transform in image edge detection)

绘制电脑中原始人声信号的时域、频域图，并在其上加入高斯白噪声，之后设计巴特沃兹滤波器将其滤除。(The draw primitive people in the computer sound signal time domain, frequency domain, and join in on the white Gaussian noise, after design Butterworth filter to filter out.)

说明：  实现多视两幅图像的surf特征点提取与匹配(Achieve surf feature point extraction and matching of two multi-view images)

opencv是图像处理中常用到的工具箱，本资料为基于此工具箱的图像处理匹配算法SURF的实现代码。(opencv is commonly used in image processing to the toolbox, the toolbox based on this information for the image processing algorithm to match the achievement of SURF code.)

实现了图像的灰度化并进一步用于视频监视控，一些自适应信号处理的算法，计算加权加速度。( Achieve a grayscale image and further control for video surveillance, Some adaptive signal processing algorithms, Weighted acceleration.)

最小噪声分离变换（Minimum Noise Fraction Rotation，MNF Rotation）工具用于判定图像数据内在的维数（即波段数），分离数据中的噪声，减少随后处理中的计算需求量(Minimum Noise Fraction Rotation (MNF Rotation) tool is used to determine the intrinsic dimension (i.e. band number) of image data, separate the noise in data, and reduce the computational requirements in subsequent processing.)

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)

基于matlab的ssim图像质量评价，验证还可以(Image quality evaluation)