rd_singlepoint

针对可见光与红外图像的特点和难点，提出了可见光与红外图像配准与融合中的关键技术，即: 使用新型的基于一维最大类间方差和最大连通性测量的图像分割方法对源图像进行分割来更好地实行图像粗 配准 使用新型的特征点提取方法，特征点的匹配及误匹配的消除来更好地实行图像精配准 采用新型的基 于区域的树状小波活性测度计算来实现树状小波图像融合 利用自生成神经网络来实现模栩图像融合. (For visible light and infrared images of the characteristics and difficulties, a visual and infrared image registration and integration of key technologies, namely: the use of new one-dimensional based on the largest variance between-class connectivity and the largest measuring method of image segmentation source image segmentation to better implementation of coarse image registration the use of a new type of feature point extraction method, feature points of the match and the elimination of false matches to better the implementation of image fine registration the use of new tree-based wavelet calculated to measure the achievement of the activity tree wavelet image fusion the use of self-generating neural network to achieve image fusion Xu mode.)

图像直方图统计，并生成灰度图。 matlab程序(Histogram of the image statistics, and generate grayscale. Matlab program)

说明：  对限制对比度自适应直方图均衡化算法adapthisteq进行了解释。可用于图像增强(Contrast to the limited adaptive histogram equalization algorithm adapthisteq is explained. Can be used for image enhancement)

PSNR图象质量评价模型，PSNR图象质量评价模型方法(Image quality evaluation model PSNR, PSNR method of image quality evaluation model)

用c语言实现一些简单的图像处理功能，如灰度化、旋转、拷贝等(Some simple image processing functions, such as gray scale, rotation, copy, etc., are implemented in C language.)

视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)

Tae Hoon Kim教授的Nonparametric Higher-Order Learning for Interactive Segmentation源码(Tae Hoon Kim, Professor Nonparametric Higher-Order Learning for Interactive Segmentation)

本案例是对人体医学断层切片的三维重建。这里的医学断层切片使用的是CT，重建的是人体躯干。(Three dimensional reconstruction of sectional slices of human medicine)

探索图像恢复中出现的正则化方法及其理论基础，进一步发展Tikhonov正则化方法，得到图像恢复算法和工程实际中一些病态问题的解，进一步发展已有的数值解 法,(Exploration of image restoration emerged regularization method and its theoretical basis, the further development of Tikhonov regularization method, the image restoration algorithm and engineering solutions of some ill-posed problem, the further development of existing numerical method,)

N-FINDR算法 端元提取 读取MNF降维后的影像数据 读取MNF降维后的影像数据( using N-FINDR end-member extraction)