N_FINDR

使用奇异值分解算法的prony算法-single value decomposition based prony algo()

【实例简介】转自CSDN分享，使用时只需改opencv路径，可成功编译。

说明：  RGB-IHS变换：图像进行 RGB-HIS 正变换，分别对明度、饱和度、色调进行线性 拉伸显示；然后再进行反变换 (RGB-IHS transformation: the image is RGB-HIS transformation, respectively, lightness, saturation, hue, a linear stretch display then the inverse transform)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and ROMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using ROMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

矫正桶形畸变，只是用了坐标变化，目前在研究张正友的算法，日后会上传的(correct the distortion of picture)

基于曲波变换，小波变换的图像融合，图像检测的相关文献，有期刊，也有硕士论文，中文英文都有，都是近几年的文章，供大家参考学习(Based on curvelet transform, wavelet transform image fusion, image detection literature, there are journals, but also master' s thesis, Chinese, English has, in recent years are the article, for your reference learning)

说明：  提出了一种基于数学形态学的多尺度熵权边缘检测方法。信息熵为图像的内在特性，由它来决定不同结构元 素所提取的边缘在最终合成边缘中所占的比重，是自适应的和客观的(A multi-scale based on mathematical morphology edge detection method of entropy. Information entropy inherent characteristics of the image, from which different structural elements to determine the edge of the extracted edge in the final synthesis of proportion, is adaptive and objective)

求二维图像的极大值点与极小值点，很好用，希望对大家有帮助(image maximum points and minimum points, very useful)

对两帧图像进行ICM调试.包含内容：1估计姿态和摄像机内参数；2两幅图做四次金字塔；3利用输入特征点采用ransac算法估计姿态R, T。4sift特征匹配；5摄像机标定。(ICM images on two debugging. Contains: 1 Estimated posture and intrinsic parameters 2 two plans to do four pyramids 3 using the input feature points using ransac algorithm estimates gesture R, T. 4sift feature matching 5 camera calibration.)

使用cnn + svm的分类，结果运行不错(Using CNN + SVM classification, the result runs pretty well.)