fingerprint-feature-extraction-

小波算法。机械化的提高我们的效率。让我们减少错误率。大家要对她有性心。(Wavelet algorithm. Mechanization to improve our efficiency. Let us reduce the error rate. We have to have sex to her heart.)

C# WPF 图片局部放大镜 示例源码

曲面拟合法是一种高精度、计算量小、抗噪性能强的方法，当前已被广泛地应用于 各种场景。比如岩石材料的变形测量、电子器件变形的测量、基于掌纹图像相关匹 配的重复定位技术等。曲面拟合法能够成功计算出亚像素位移的前提是整像素搜索阶 段可以正确搜索出模板的整像素匹配点，一旦整像素匹配点搜索出现错误，那么亚像素 测量阶段所得到的位移距离就会失去意义。 使用不同的拟合函数对相关系数矩阵进行曲面拟合，会对结果产生一定的影响，比 较常用的拟合函数有二次函数、三次函数、高斯函数等。(Surface fitting method is a high-precision, computational complexity is small, strong anti-noise performance method, has been widely used in various scenes. For example, deformation measurement of rock materials, deformation measurement of electronic devices, and repetitive location technology based on palmprint image correlation matching. Surface fitting method can successfully calculate the sub-pixel displacement on the premise that the whole pixel search stage can correctly search the template of the whole pixel matching point, once the whole pixel matching point search error, then the sub-pixel measurement phase of the displacement distance will be meaningless. Using different fitting functions to fit the surface of the correlation coefficient matrix will have a certain impact on the results. The commonly used fitting functions are quadratic function, cubic function, Gaussian function and so on.)

超分辨率中要使用的分块重叠及恢复，任意像素重合，超简单！(Super-resolution block overlap and recovery, to use in any pixel overlap, super easy!)

对多目标航迹跟踪进行关联和融合,采用JPDA（联合概率数据关联算法）对目标跟踪(And the multi-target track tracking is combined and merged)

利用滑模变控制去实现风机的最大功率跟踪。基本原理是控制tip-speed ratio(The use of Sliding Mode Variable fan control to achieve maximum power tracking. The basic principle is to control the tip-speed ratio)

Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.

matlab加载一个视屏，转化成每帧图片加以保存。(Matlab loads a video screen and translates it into pictures for each frame to be saved.)

k-均值聚类法用于各种图像的聚类、分割问题，希望可以对您有利(k-means clustering method for a variety of image clustering, segmentation)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)