图像拼接技术

使用EM算法完成图像的分割，使用混合高斯模型，可以修改分类数。运行EM.m(Expectation Algorithm)

基于均值漂移算法实现图像的分割，总共带有三个文件，其中两个是两个子程序(Based on mean-shift image segmentation algorithm, with a total of three documents, two of which are the two subroutines)

matlab code for the v shape recognition in heart shape

此代码采用正态分布变换算法NDT进行点云配准(This code uses the normal distribution transform algorithm NDT for point cloud registration)

这个算法包实现了从多幅拥有共同结构的图像里提取出共同形状的骨架。( This package implements the algorithm for Sketching the Common as described in: Shai Bagon, Or Brostovsky, Meirav Galun and Michal Irani "Detecting and Sketching the Common", CVPR 2010)

指纹识别中的指纹图像预处理程序，包括有归一化，二值化，图像分割，图像增强，图像细化(Fingerprint identification fingerprint image preprocessing procedures, including normalization, binarization, image segmentation, image enhancement, image thinning)

利用sift算法提取两张图像的特征点，利用ransac去除误匹配，最后将两张图片拼接在一起(Using sift algorithm to extract the two feature points of the image, the use of ransac to remove the wrong match, and finally the two pictures together)

通过高斯核密度估计计算多元变量之间的互信息熵(The mutual information entropy between multivariate variables is calculated by Gaussian kernel density estimation)

针对同一场景可见光与红外图像的融合问题,提出了一种新的多尺度对比度塔图像融合方法. 该方法 利用对比度金字塔数据结构得到图像的多分辨序列,采用基于视觉特性的融合算子在图像的相应各级上融 合源图像的细节,再通过金字塔逆变换重构出最终融合图像. 这种图像处理方法具自适应性,不随各自输入 图像的灰度特性而改变,同时增强了融合图像的对比度,产生了较好的视觉处理效果.(for the same scene of visible and infrared images of integration, proposed a new multi-scale tower contrast images. The use of contrast pyramid data structure Image multiresolution sequence, based on visual characteristics of the integration operator of the image corresponding levels of integration details of the source image, through the inverse pyramid transform eventually fused images. This image processing method has adaptability without their input image with the gray characteristics change, while enhancing the integration of the contrast of the image. have a better visual effect.)

pcnn 彩色图像分割中，是非常有用的一个程序！！！！！！(PCNN is applied to the color image segmentation.it is very very very very useful!)