Codes

本程序是用似然比检验的方法进行profile中的变点检测(This procedure is used likelihood ratio test method of change point detection profile)

寻找最低像素值的matlab实现：通过图像矩阵的循环查找，能实现最低像素值的快速寻找(Matlab find the lowest pixel value to achieve: to find the cycle through the image matrix, to achieve the minimum pixel value Quick Find)

说明：  MATLAB PIV处理程序，可以方便处理PIV得到图像的流场信息(Matlab PIV processing program can easily process PIV to get the flow field information of the image)

Computing regularization paths for learning multiple kernels另一个多核学习算法 可用于多类分类 目标检测(Computing regularization paths for learning multiple kernels The other multicore learning algorithm for multi-class classification target detection)

db4小波的提升小波分解，从已发表的论文中得来，可放心使用(db4 wavelet lifting wavelet decomposition, the published papers come, you can be assured)

说明：  小波分析增强滤波。小波变换的原理类似傅里叶变换，只是把三角函数基换成了小波基。与傅里叶变换不同，小波变换有两个变量：scale和translation。scale控制小波函数的收缩，其导数即为频率，translation控制小标函数的平移，平移量对应时间。通过信号的伸缩平移，可以得到某种重合情况，这样积分也会得到一个极大值，不同的是，得到频率成分的同时，还可以知道该频率的时间位置。(Wavelet analysis enhances filtering.The wavelet transform works like the Fourier transform, except that the trigonometric basis is replaced by a wavelet basis. Unlike the Fourier transform, the wavelet transform has two variables: scale and Translation. Scale controls the contraction of the wavelet function, its derivative is frequency, translation controls the translation of the small scale function, and the translation corresponds to the time. By stretching and shifting the signal, you can get some kind of coincidence, so that the integral will also get a maximum, but the difference is that when you get the frequency component, you can also know the time position of that frequency.)

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

转换八位256色位图源码，例程程序置入汇编代码，通过图像字节集数据的计算转换为八位256色位图。(Conversion of eight 256-color bitmap source , routine program into assembly code to calculate the conversion by the image data into eight byte set 256-color bitmap .)

Matlab源代码 Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision (IJCV), vol.77, no.1-3, pp.125-141, 2008.(Matlab source code for Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision (IJCV), vol.77, no.1-3, pp.125-141, 2008.)

采用canny算子加上lee滤波器对SAR图像进行去噪(canny algorithm and lee filter to aiti-noise SAR image)