DLT-matlab

VisualC++数字图像处理技术详解第2版光盘-第六章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VI)

基于C++的人脸分割的设计，C／Ｃ++语言、数字信号处理，最好选修过“数字图像处理”。(Based on the C++ face segmentation design, C/C++ language, digital signal processing, the best elect digital image processing.)

完成流程编码和huffman编码，适合做图像编码的熵编码，只要调整接口就好。(Complete the process encoding and huffman coding, image coding suitable for entropy coding, as long as the adjustment of the interface is like.)

一种求解电大平台天线互耦的时域积分方程快速算法(Solving the Antenna TV platform stie fast algorithm)

说明：  傅立叶变换在图像处理中的重要作用： (A) 图像增强与图像去噪 绝大部分噪音都是图像的高频分量，通过低通滤波器来滤除高频噪声；边缘也是图像的高频分量，可以通过添加高频分量来增强原始图像的边缘； (B) 图像分割与边缘检测 提取图像高频分量 (C) 图像特征提取 形状特征：傅里叶描述子 纹理特征：直接通过傅里叶系数来计算纹理特征 其他特征：将提取的特征值进行傅里叶变换来使特征具有平移、伸缩、旋转不变性 (D ) 图像压缩 可以直接通过傅里叶系数来压缩数据；常用的离散余弦变换是傅立叶变换的实变换； (Fourier transform in the important role of image processing: (A) image enhancement and image denoising Most of all images of high frequency noise by low pass filter to filter out high frequency noise edge is the image of the high frequency components can be enhanced by adding high frequency components to the edge of the original image (B) Image segmentation and edge detection High frequency components extracted image (C) image feature extraction Shape features: Fourier descriptors Texture: directly through the Fourier coefficients to calculate the texture features Other features: The characteristic value extracted by Fourier transform to the characteristics of translation, scaling, rotation invariance (D) image compression Fourier coefficients can be directly compressed data commonly used discrete cosine transform is the Fourier transform of the real transformation 朗读显示对应的拉丁字符的拼音)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

基于菲涅耳全息图和离散余弦变换的_盲数字水印_技术(Blind digital watermarking _ technology based on the Fresnel hologram and the discrete cosine transform _)

该代码用于对错误匹配点进行过滤操作，即去除错误的匹配点(The code is used to remove the wrong match points,and make sure the right matching.)

IDL编程：tif图像批量layerstacking，并保留原有地图投影。使用时只需要修改输入和输出路径。(IDL programming: tif image batch layerstacking, and retain the original map projection. Only need to modify the input and output paths use.)