SP_Liu_TCSVT_code2014

说明：  SURF的实现代码,用于图像特征提取，对于入门计算机视觉的同学有帮助(SURF implementation code for image feature extraction, for beginners of computer vision students help)

说明：  Kirsch算子边缘检测代码，计算了八个方向的梯度值(Kirsch operator edge detection code)

说明：  基于python-opencv的图像匹配代码，包含了SIFT、SURF、AKAZE、ORB方法。43-48行修改图像路径，74-81修改匹配方法。(dev: python3.6+opencv_python==3.4.2+opencv-contrib-python==3.4.2)

简单的bmp图象处理程序，将一个bmp文件中的直线和园改变颜色(Bmp simple image processing procedures, will be a bmp file in a straight line and Park change color)

基于模板匹配的另一种人脸识别的算法，很实用，利用肤色建模来建立人脸皮肤模型(Another based on template matching algorithm for face recognition, very practical, using skin color modeling to create a human face skin model)

说明：  Donoho提出的小波阀值去噪的基本思想是将信号通过小波变换（采用Mallat算法）后，信号产生的小波系数含有信号的重要信息，将信号经小波分解后小波系数较大，噪声的小波系数较小，并且噪声的小波系数要小于信号的小波系数，通过选取一个合适的阀值，大于阀值的小波系数被认为是有信号产生的，应予以保留，小于阀值的则认为是噪声产生的，置为零从而达到去噪的目的。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。(The basic idea of wavelet threshold de-noising proposed by Donoho is that the wavelet coefficients generated by the signal after passing through the wavelet transform (Mallat algorithm) contain the important information of the signal. After the wavelet decomposition, the wavelet coefficients of the signal are larger, the wavelet coefficients of the noise are smaller, and the wavelet coefficients of the noise are smaller than the wavelet coefficients of the signal. By selecting a suitable threshold, the wavelet coefficients of the noise are larger than the valve The wavelet coefficients of values are considered to be generated by signals, which should be preserved. The ones less than the threshold are considered to be generated by noise, which is set to zero to achieve the purpose of denoising.)

算法实现消除小面积的区域,保留面积大的区域.对去除小斑点很稳健.(Algorithm to achieve the elimination of small areas of the region to retain the large area of the region. For the removal of small spots very sound.)

说明：  求矩阵的特征值和特征向量，使用c语言编程(Matrix eigenvalues and eigenvectors, c language programming)

CVPR2011文章Efficient_Rotation_Invariant_Object_Detection_using_Boosted_Random_Ferns(paper of CVPR2011 Efficient_Rotation_Invariant_Object_Detection_using_Boosted_Random_Ferns)

实现六自由度运动学逆解算法，汽车课设货车Matlab驱动力图程序，对信号进行频谱分析及滤波。( Six degrees of freedom to achieve inverse kinematics algorithm, Car class-based truck driver trying to Matlab program, The signal spectral analysis and filtering.)