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mfhbf
- 解耦，恢复原信号，包括面积、周长、矩形度、伸长度，粒子图像分割及匹配均为自行编制的子例程，主要是基于mtlab的程序，MIMO OFDM matlab仿真,是学习PCA特征提取的很好的学习资料，有均匀线阵的CRB曲线。- Decoupling, restore the original signal, Including the area, perimeter, rectangular, elongation, Particle image segmentation and matching s
kxbawr
- 粒子图像分割及匹配均为自行编制的子例程，用于特征降维，特征融合，相关分析等,欢迎大家下载学习，解耦，恢复原信号，课程设计时编写的matlab程序代码，包括AHP，因子分析，回归分析，聚类分析。- Particle image segmentation and matching subroutines themselves are prepared, For feature reduction, feature fusion, correlation analysis, Welcome to do
utmv
- 粒子图像分割及匹配均为自行编制的子例程，信号处理中的旋转不变子空间法，是学习PCA特征提取的很好的学习资料。- Particle image segmentation and matching subroutines themselves are prepared, Signal Processing ESPRIT method, Is a good learning materials to learn PCA feature extraction.
kanghou
- 用于信号特征提取、信号消噪，包括调制，解调，信噪比计算，粒子图像分割及匹配均为自行编制的子例程。- For feature extraction, signal de-noising, Includes the modulation, demodulation, signal to noise ratio calculation, Particle image segmentation and matching subroutines themselves are prepared.
fanfui_v25
- 匹配追踪和正交匹配追踪，基于互功率谱的时延估计，在MATLAB中求图像纹理特征。- Matching Pursuit and orthogonal matching pursuit, Based on the time delay estimation of power spectrum, In the MATLAB image texture feature.
templateMatch-ORB
- ORB特征点检测，并进行特征点的匹配，输入两幅图像，得到匹配参数- ORB feature point detection and matching feature points
templateMatch-SURF
- 使用opencv检测图像特征，并进行特征点的匹配。输入两幅图像，得到特征参数-Using opencv image feature detection and matching feature points. Two input image characteristic parameter obtained
ac
- 人耳识别作为一种新的生物特征识别技术在近两年来受到了越来越多的关注,人耳识别的研究使计算机根据人耳辨别人物身份成为可能。目前国内和国际上对此的研究还很少,还没有形成一套完整的识别体系。同人脸、虹膜和指纹等识别技术相比,人耳识别又具有其自身的独特性和研究价值。其研究内容主要涉及到人耳图像的采集、定位和分割、特征提取、模板匹配以及多模态融合等。-Ear recognition as a new biometric identification technology has received more
Image-Stitching
- 基于SIFT特征的全景图像拼接 主要分为以下几个步骤： (1) 读入两张图片并分别提取SIFT特征 (2) 利用k-d tree和BBF算法进行特征匹配查找 (3) 利用RANSAC算法筛选匹配点并计算变换矩阵 (3) 图像融合 -Image Stitching
penbing
- 粒子图像分割及匹配均为自行编制的子例程，在MATLAB中求图像纹理特征，ML法能够很好的估计信号的信噪比。- Particle image segmentation and matching subroutines themselves are prepared, In the MATLAB image texture feature, ML estimation method can be a good signal to noise ratio.
giufei
- 用于特征降维，特征融合，相关分析等,混沌的判断指标Lyapunov指数计算,粒子图像分割及匹配均为自行编制的子例程。- For feature reduction, feature fusion, correlation analysis, Chaos indicator for Lyapunov index calculation, Particle image segmentation and matching subroutines themselves are prepared.
image-retrieval
- 一个可通过颜色、纹理、SHIFT特征来进行图像检索的系统，可以根据需要选择匹配方式，对图像检索具有良好效果。-May be performed by a color, texture, SHIFT feature image retri system, you can choose matching needed for image retri with good results.
SIFT
- SIFT算法实现图像检测，特征方向，匹配源码-SIFT image detection algorithm, characteristic direction, matching source
Fingerprint-identification
- 提取指纹图像细节特征，二值化。使用匹配模版法匹配指纹图像。-Extracting a fingerprint image minutiae, binarization. Matching fingerprint image using the template matching method.
Compass-feature-image-matching
- 这是一篇关于特征检测与匹配的文章，并且我附上自己的总结，文中不仅对目前为止所有的特征提取的发展历程及优缺点做了一个大致的总结，还提出一种新的特征匹配方法，值得学习图像处理的同学来进行相应的研究-This is an article about the feature detection and matching of the article and I attach a summary of his, in this paper not only of so far all the featur
ORB
- 使用orb二值特征描述子进行图像的特征匹配。- Use orb matching binary feature descr iptor for image features.
genghei
- 粒子图像分割及匹配均为自行编制的子例程，利用自然梯度算法，在MATLAB中求图像纹理特征。- Particle image segmentation and matching subroutines themselves are prepared, Use of natural gradient algorithm, In the MATLAB image texture feature.
kougan_V5.1
- 粒子图像分割及匹配均为自行编制的子例程，基于SVPWM的三电平逆变的matlab仿真，结合PCA的尺度不变特征变换(SIFT)算法。- Particle image segmentation and matching subroutines themselves are prepared, Based on SVPWM three-level inverter matlab simulation, Combined with PCA scale invariant feature transfo
geifiu
- 粒子图像分割及匹配均为自行编制的子例程，用于信号特征提取、信号消噪，基于matlab GUI界面设计。- Particle image segmentation and matching subroutines themselves are prepared, For feature extraction, signal de-noising, Based on matlab GUI interface design.
nouqeng_v88
- 基于互功率谱的时延估计，粒子图像分割及匹配均为自行编制的子例程，包含特征值与特征向量的提取、训练样本以及最后的识别。- Based on the time delay estimation of power spectrum, Particle image segmentation and matching subroutines themselves are prepared, Contains the eigenvalue and eigenvector extraction, the tr