image-processing

基于小波模极大值贝叶斯阈值算法，实现图像吐糟的功能，与其他去噪方法进行对比。(Based on wavelet modulus maxima Bayesian threshold algorithm, spit bad image features comparison with other denoising methods.)

实现了读取一个bmp图片的傅里叶变换 使用C语言实现 读取一个 24位 图 转换为8位灰度图后，进行傅里叶变换处理（二维） 采用快速傅里叶变换——蝶形算法 北航图像处理作业~需要进一步交流 请联系qq 422563357(Reads a bmp image to achieve a Fourier transform using C language Northern image processing operations)

极化SAR图像处理中，将协方差矩阵转换为STOKES矩阵或Mueller矩阵的程序(Polarization SAR image processing, the covariance matrix will be converted to matrix or Mueller matrix STOKES procedures)

一种基于改进DRLSE的运动目标分割方法,为了得到精准、连续的运动目标轮廓，提出将帧间差分法和改进的距离规则水平集演化(DRLSE)方法结合应用于运动目标分割。(In order to get an accurate and continuous contour of moving target, a novel method to segment moving object by combining the frame difference and an improved distance regularized level set evolution (DRLSE) is proposed. )

水平集图像分割代码，对应论文为Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: a new variational formulation[C]//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005, 1: 430-436.(Level set image segmentation code, the corresponding papers of Li C, Xu C, Gui C, et al Level set evolution without re-initialization:.. A new variational formulation [C]// Computer Vision and Pattern Recognition, 2005 CVPR 2005 IEEE. Computer Society Conference on IEEE, 2005, 1:. 430-436.)

Downsample image console program using opencv.

说明：  变速恒频双馈风力发电机基于电子磁链定向的空载并网仿真模型(Variable speed constant frequency doubly-fed wind generators no-load parallel simulation model )

说明：  360°图像序列的柱面全景拼接算法，matlab仿真程序。(360 image sequence cylindrical panoramic mosaic algorithm, matlab simulation program.)

实现了对10个数字音的识别，是学习PCA特征提取的很好的学习资料，包括调制，解调，信噪比计算。( To achieve the recognition of 10 digital sound, Is a good learning materials to learn PCA feature extraction, Includes the modulation, demodulation, signal to noise ratio calculation.)

颜色、形状、纹理特征提取算法及应用,各种方法都有，很有用。(Color, shape, texture feature extraction algorithms and applications, various methods are very useful.)