DCT

winform 仿QQ截图，可将截图直接保存到内存中 附完整源码下载

图像融合中pan图像和多光谱融合的四种方法（IHS PCA 小波 小波结合PCA）和融合效果的评价指标计算（信息熵、Q4等指标）(Image fusion of the pan and multi-spectral image with four methods (IHS PCA wavelet wavelet combined PCA) and fusion effect evaluation method)

利用光流法在复杂场景下对运动目标进行检测(Using the optical flow method to detect moving objects in complex scenes)

一种二维EMD算法，作者是Anna Linderhed，该学者博士研究课题为二维EMD和小波变换，她的二维EMD算法与Nenus或其他学者的算法有较大的不同。(A two-dimensional EMD algorithm, the author is Anna Linderhed, her Ph.D. research topic is bidimensional EMD and wavelet transform. Her two-dimensional EMD algorithm is quite different from that of Nenus and other scholars.)

物体分割，Ross Girshick的R-CNN + fast R-CNN + faster R-CNN(object classification Ross Girshick. R-CNN+ fast R-CNN+ faster R-CNN)

多尺度LBP，代码根据论文“Learning Multi-scale Block Local Binary Patterns for Face Recognition”编写的(this code is obtained from the paper named "Learning Multi-scale Block Local Binary Patterns for Face Recognition")

B样条曲线分为近似拟合和插值拟合，所谓近似拟合就是不过特征点，而插值拟合就是通过特征点，但是插值拟合需要经过反算得到控制点再拟合出过特征点的B样条曲线方程。这里会一次介绍两种拟合算法。

该文件夹包括代码及其对应的论文。其作用在于模拟人类视觉系统的生理特性--视觉注意机制，按照人眼观察外界的方式，检测视觉显著性物体和区域，并阐述了显著性区域的显著性密度和尺度之间的关系，可应用于生物视觉模拟、视觉目标检测、视觉目标跟踪、视觉智能监控，以及视觉生理学和视觉心理学等的研究中。(This document contains codes and the corresponding paper. The aim is to simulate a physiological characteristic of human visual system called visual attention mechanism. The code is used to detect salient objects or regions following the way human eye observes the world. The relation between density and scale of salient object/region is described. It has found widespread use in numerous applications such as biological vision simulation, visual object detection, visual object tracking, visual intelligent surveillance, visual physiological, and visual psychology.)

说明：   最简单的差分格式有向前、向后和中心3种。 向前差分：f (n)=f(n+1)-f(n) 向后差分：f (n)=f(n)-f(n-1) 中心差分：f (n)=[f(n+1)-f(n-1)]/2(The easiest difference format forward, backward, and three kinds of centers. Forward differencing: f (n) = f (n+ 1)-f (n) Backward difference: f (n) = f (n)-f (n-1) Central difference: f (n) = [f (n+ 1)-f (n-1)]/2)

傅里叶时域频域变换图形处理S参数在时域的应用(Fourier time domain frequency domain transform graphics processing S-parameters in the application of the time-domain)