KernelFisher

说明：  基于MATLAB，对逐日的sst遥感数据做月平均并制作gif动图(Based on MATLAB, the daily SST remote sensing data are averaged monthly and the GIF map is made)

说明：  模糊C均值聚类算法的MATLAB实现，比较经典的程序(Fuzzy C-means clustering algorithm to achieve MATLAB, compare the classic procedure)

拉普拉斯变换是工程数学中常用的一种积分变换，又名拉氏转换。拉氏变换是一个线性变换，可将一个有引数实数t（t≥ 0）的函数转换为一个引数为复数s的函数。(Laplace transform is a commonly used engineering mathematics integral transformation, also known as Laplace Transform. Laplace transform is a linear transform, argument can be a real number t (t ≥ 0) is converted to a function of a complex number s argument of the function.)

此代码采用正态分布变换算法NDT进行点云配准(This code uses the normal distribution transform algorithm NDT for point cloud registration)

通过循环去多个种子点实现基于区域生长的图像分割(Achieve image segmentation based on region growing cycle to go through multiple seed points)

图像处理中经常需要检测圆形状的物体，该程序用霍夫变换可以检测圆的圆心和半径。(Image processing often requires detection of a round-shaped objects, the program can detect the use of Hough transform and radius of the center circle.)

指纹分类是指纹准确识别的前提，该程序是指纹分类源码(Fingerprint classification is a prerequisite to accurately identify the fingerprint, fingerprint classification code of the program is)

做完边缘提取之后，有的边缘断断续续，需要进行边缘连接，将其连成完整线。(Done after edge detection, and some off the edge, the edge needed to connect, to link into the full line.)

对较真实的运动模糊图像进行恢复，此种运动模糊图像限于水平或垂直的运动模糊图像。 参考文献：Removing Camera Shake from a Single Image ,siggraph06(On the more realistic motion-blurred image restoration, such motion-blurred images limited to horizontal or vertical motion-blurred images. References: Removing Camera Shake from a Single Image, siggraph06)

图像分割是把图像划分成具有实际意义的互补交迭的区域的集合。在图像分割之前，图像区域的数目未知，而在分割后各个区域同时满足均匀性和连通性的条件，故图像分割是一个复杂的过程，目前大多数研究都是针对某一类型图像或者某一具体应用的分割。图像阈值化分割的基本思想是确定一个阈值，然后把每个像素点的像素值和阈值相比较，根据比较的结果把像素划分为两类，前景(1)或背景(0)。该方法的关键是确定一个最优的阈值。常用的阈值确定方法有直接门限法、类间最大方差法(otsu法)、分水岭算法、最小误差法、最大熵法等。该段代码就是图像的阈值分割。(The image segmentation is divided into the practical significance of the emerging area of complementary set. In the image segmentation, before the number of image region, the unknown, and after each area in division at the same time satisfy the uniformity and connectivity condition, so the image segmentation is a complicated process now, most research are for a certain type image or a particular application of the division. Image threshold segmentation of the basic idea is to determine a threshold, then put each pixel threshold value of the pixels and compared, according to the result of the comparison the pixel divided into two categories, the prospect (1) or background (0). This method is the key to determine an optimal threshold. Commonly used method to determine the threshold value of direct threshold method, the largest of the variance between method (otsu method), watershed algorithm, the minimum error method, the maximum entropy method, etc. When the code is image threshold segm)