bandpass-filters

捷宇 高拍仪 demo SDK

基于双目视觉的三维重建，包含了重建过程中的基本步骤。(Binocular vision-based three-dimensional reconstruction, including the reconstruction of the basic steps of the process.)

卡尔曼滤波算法，opencv代码，鼠标跟踪(Kalman filter algorithm, opencv code, mouse tracking)

该源代码包含图像10个不变矩的图像检索，小波模极大值法边缘检测，结合颜色特征与不变矩的图像检索源程序(The source code includes images of invariant moments 10 image retrieval, wavelet modulus maximum edge detection method, combining color characteristics and the moment invariants image retrieval source program)

MATLAB图像处理程序，批量将彩色图片转换成灰色图像，用于其他图像处理程序(MATLAB image processing, batch convert color pictures of the gray image used for other image processing program)

图像反卷积 来自D. Krishnan, R. Fergus中提到的方法(image deconvolution The Matlab functions in this directory solve the deconvolution problem in the paper D. Krishnan, R. Fergus: "Fast Image Deconvolution using Hyper-Laplacian Priors", Proceedings of NIPS 2009. )

离散分数阶傅立叶变换，算法参考 C. Candan, M.A. Kutay, and H.M. Ozaktas. The discrete Fractional Fourier Transform. IEEE Trans. Sig. Proc., 48:1329--1337, 2000 (Discrete fractional Fourier transform algorithm reference C. Candan, MA Kutay, and HM Ozaktas. The discrete Fractional Fourier Transform. IEEE Trans. Sig. Proc., 48:1329- 1337, 2000)

说明：  实现了平移、缩放、旋转和错切等仿射变换的问题，并用MATLAB逼真模拟了蕨类叶、变形蕨类叶与拟仿射变换分形图。(Achieved a pan, zoom, rotate and cross cutting issues such as affine transformations, and use MATLAB to simulate real fern leaf, fern leaves and to be deformed affine fractal images.)

说明：  水平集边缘检测代码，利用了变分法进行优化和处理，实现了图像的快速边缘检测(Level set edge detection code is optimized and processed by using variational method, and fast edge detection of image is realized.)

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)