lianmazhixianjiance

说明：  基于局部Fisher准则的非线性核Fisher辨别分析，应用于有监督的特征提取与高维数据的有效降维。(Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction.)

说明：  VC++数字图像处理典型算法及实现 01 源程序(VC++ Digital Image Processing Algorithm and Implementation of a typical 01)

基于凸集投影算法的超分辨率图像重建算法，在现有算法基础上改进计算精度和效率。(To improve the computational accuracy and efficiency in the existing algorithms based on super-resolution image reconstruction algorithm based on projections onto convex sets algorithm.)

这是一种彩色图像去雾算法，可以识别物体的轮廓，但是同时也会加强这些轮廓，虽然去雾效果不错，但是会加强轮廓！(This is a color image to fog algorithm can identify the contours of the object, but will also enhance the outlines, although to fog effect is good, but will strengthen the profile!)

PIVlab - 时间分辨粒子图像测速（PIV）工具： 一种基于GUI的工具，用于预处理，分析，验证，后处理，可视化和模拟PIV数据。 使用MATLAB网络研讨会进行人脸识别代码： 使用MATLAB在线讲座的人脸识别中的主要演示文件。 Gabor特征提取： 该程序生成一个自定义Gabor滤波器组; 并使用它们提取图像特征。 主成分分析： 用于特征提取； 链码： 基于MATLAB的freeman的曲面轮廓描述(PIVlab - time resolved particle image velocimetry (PIV) tool: A GUI based tool for preprocessing, analysis, validation, post processing, visualization, and Simulation of PIV data. Using MATLAB webinar for face recognition code: The main demo file is used in MATLAB online lectures for face recognition. Gabor feature extraction: The program generates a custom Gabor filter bank and uses them to extract image features. Principal component analysis: For feature extraction; Chain code: Surface contour description of Freeman based on MATLAB)

C语言图形、图像函数 功能实用全面介绍(C language graphics, images, full function functional and practical introduction)

本例程序包含OMP、SP、IRLS、CoSaMP等重构算法，比较性能优劣。(The example application consists of OMP, SP, IRLS, CoSaMP reconstruction algorithm, and compare performance advantages and disadvantages. )

说明：  利用卷积神经网络进行特征提取，并将卷积特征可视化(Feature extraction using convolutional neural networks and visualization of convolution features)

在IDL中运行，可以将多光谱、高光谱等多波段影像拆分为为单一波段影像(It works in IDL and it can split multibands into single band)

SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here. Table of Contents Installation Datasets Train Evaluate Performance Demos Future Work Reference