manface-feature-extraction--examples

贝叶斯matlab程序设计算法，给出了各种实验数据和实验方法。(Bayesian matlab programming algorithm, given a variety of experimental data and experimental methods.)

循环流化床锅炉燃烧过程simulink模型 解耦以后的(CFBB combustion simulink model )

特征提取实例中的确定圆形目标源程序，能有效提取出图像中的圆形目标，并标记(Feature extraction instance to determine circular target source, can effectively extract the image of circular target, and mark)

自己做的双目标定的MATLAB标定实例，附带图像和仿真程序！(Make their own calibration of the binocular calibration of MATLAB examples, incidental images, and simulation program!)

该程序用matlab画出clarke模型下，信号理论自相关函数与仿真情况下自相关函数的情形对比。(The program with matlab draw clarke model, signal theory and simulation of the autocorrelation function of the autocorrelation function under the circumstances contrast.)

fuzzy damn in matlab

This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations

求满足巴特沃斯模拟低通的最低结束以及滤波器系统函数及其几点位置(To meet the butterworth lowest end and analog low-pass filter system function and its several location)

dtw的matlab算法，识别率达到100 。可直接使用或转成c语言代码，下到下位机运行。(dtw' s matlab algorithm to achieve 100 recognition rate. Can be used directly or converted into c language code, run down to the lower machine.)

说明：  多焦点图像融合 利用基于双边梯度的锐化标准 相当对应的论文是J. Tian, L. Chen, L. Ma and W. Yu, "Multi-focus image fusion using a bilateral gradient-based sharpness criterion,"(This is a demo program of the paper J. Tian, L. Chen, L. Ma and W. Yu, "Multi-focus image fusion using a bilateral gradient-based sharpness criterion," Optics Communications, Vol. 284, Jan. 2011, pp. 80-87.)