搜索资源列表
qzeykdzz
- 有均匀线阵的CRB曲线，验证可用，D-S证据理论数据融合，考虑雨衰 阴影 和多径影响，能量熵的计算，单径或多径瑞利衰落信道仿真，多姿态，多角度，有不同光照,借鉴了主成分分析算法（PCA）。- There ULA CRB curve, Verification is available, D-S evidence theory data fusion, Consider shadow rain attenuation and multipath effects Energy entropy cal
benqen
- D-S证据理论数据融合，雅克比迭代求解线性方程组课设,基于多相结构的信道化接收机。- D-S evidence theory data fusion, Jacobi iteration for solving linear equations class-based, Channelized receiver based on multi-phase structure.
miefun
- 一种基于多文档得图像合并技术，光纤无线通信系统中传输性能的研究，用于特征降维，特征融合，相关分析等。- Based on multi-document image obtained combining technique, Fiber Transmission wireless communication system performance, For feature reduction, feature fusion, correlation analysis.
image_fusion
- 研究了基于多尺度分析的图像融合算法体系，以及离散小波变换的移变性原因，提出了一种混合融合算法。-Studied the image fusion algorithm based on multi-scale analysis system, and move the degeneration reasons of discrete wavelet transform, a hybrid fusion algorithm is proposed.
PSF
- 提出一种基于空间域的多聚焦图像融合技术：首先计算点分布函数（PSF），然后利用PSF模糊源图像，最后通过组合原图像中较为清晰的像素点来生成全聚焦图像。-We propose a multi-focus image fusion based on space domain techniques: First calculate the point distribution function (PSF), then use the PSF fuzzy source image, and finall
genghei
- AHP层次分析法计算判断矩阵的最大特征值,基于多相结构的信道化接收机，用于特征降维，特征融合，相关分析等。- Calculate the maximum eigenvalue judgment matrix of AHP, Channelized receiver based on multi-phase structure, For feature reduction, feature fusion, correlation analysis.
fmeasure
- Contains the matlab code for various focus measures used in image processing applications such as multi focus image fusion/autofocus and so on. It has the implementations according to the paper (http://www.sciencedirect.com/science/article/pii/S003
jangnen_v22
- 可直接计算得到多重分形谱,相控阵天线的方向图（切比雪夫加权），D-S证据理论数据融合。- It can be directly calculated multi-fractal spectrum, Phased array antenna pattern (Chebyshev weights), D-S evidence theory data fusion.
jeijiu_v73
- 数学方法是部分子空间法，多机电力系统仿真及其潮流计算，用于特征降维，特征融合，相关分析等。- Mathematics is part of the subspace, Multi-machine power system simulation and flow calculation, For feature reduction, feature fusion, correlation analysis.
my_new_program
- 用于多焦点的图像融合，程序经调试后准确无误-For multi focus image fusion, the program after debugging accurate
multiple_object_tracking
- 用matlab仿真多目标跟踪中的航迹关联融合的程序，-Matlab simulation of multi-target tracking with the track association fusion procedure
ProtectionForBelt_BasedOnPLC
- 带式输送机断带抓捕程序，采用一主多从的PLC模式，对皮带速度、张紧力进行多传感信息融合，获取皮带的实时状态，断带时进行抓捕杜绝误抓捕情况。-Belt Conveyor Breakage and arrest procedures, a master multi-mode PLC from, the speed of the belt tensioning force multisensor information fusion, get real-time status of the belt,
gradient-HDR
- 基于梯度的多曝光图像融合，不需要计算相机相应函数和色调映射，采用梯度即可获得较好的效果-You can get better results based on gradient Multi exposure image fusion, the camera does not need to calculate the appropriate functions and tone mapping, gradient
2016-ZhouZhiqiang-Hybrid_MSD_Fusion
- Zhouzhiqiang的图像融合MATLAB源码，对应其2016年发表的论文Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters-Perceptual fusion of infrared and visible images through a hybrid multi-scale decom
2014-ZhouZhiqiang-MWGF
- ZhouZhiqiang2014年发表论文及源码，论文为基于多尺度加权梯度的多聚焦图像融合Multi-scale weighted gradient-based fusion for multi-focus images-Multi-scale weighted gradient-based fusion for multi-focus images
HDRfusion
- 基于retinex的多曝光融合matlab算法-Multi-Exposure Image Fusion Based on Illumination Estimation
giulang_v26
- 包含收发两个客户端的链路级通信程序,用于特征降维，特征融合，相关分析等,一种基于多文档得图像合并技术。- Contains two clients receive link-level communications program, For feature reduction, feature fusion, correlation analysis, Based on multi-document image obtained combining technique.
ningjing_v27
- 多姿态，多角度，有不同光照,有CDF三角函数曲线/三维曲线图，D-S证据理论数据融合。- Much posture, multi-angle, have different light, There CDF trigonometric curve/3D graphs, D-S evidence theory data fusion.
ICIP_fusion
- MULTI-EXPOSURE IMAGE FUSION:APATCH-WISE APPROACH by Kede Ma
Saliency-Detection
- 提出一种新的显着性检测方法，通过将区域级显着性估计和像素级显着性预测与CNN（表示为CRPSD）相结合。对于像素级显着性预测，通过修改VGGNet体系结构来执行完全卷积神经网络（称为像素级CNN）以执行多尺度特征学习，基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计，首先设计基于自适应超像素的区域生成技术以将图像分割成区域，基于该区域通过使用CNN模型（称为区域级CNN）来估计区域级显着性。通过使用另一CNN（称为融合CNN）融合像素级和区域级显着性以形成nal显着图，并