搜索资源列表
remoteSensing
- 一种新的彩色图像特征检测算法 -学术论文 - 图像图形网-机器视觉,数字水印,遥感,指纹,人脸识别,生物医学,神经网络,人工智能,GIS,小波变换-a new color image feature detection algorithm-academic-Image Network Graphics-machine vision, digital watermark, remote sensing, fingerprint, face recognition, biomedical, neur
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- 一种图像检索中纹理特征提取的方法。本文介绍了基于Gabor 滤波器和Gabor 小波变换提取纹理特征的分析方法, 以及对Gabor 小波进行了高斯归一化以提高对图像检索的速度和准确度。-An image retrieval texture feature extraction methods. This article based on Gabor filters and Gabor wavelet transform to extract texture feature analysis me
FeatureExtraction
- Feature Extraction of Infrared Target Based on Image Moment and Wavelet Energy
MFCCcanshu
- 6篇有关语音识别特征参数的科技论文，主要包括MFCC参数LPCC参数以及小波应用于特征提取的研究-6 on the speech recognition feature parameters of scientific papers, including MFCC parameters LPCC parameters and wavelet feature extraction applied to the study
04kz2612
- 基于小波包特征提取的车牌字符识别，是一片期刊论文，还不错的，可以学习-Feature extraction based on wavelet packet license plate character recognition is a journal articles, but also good, you can learn
neuralandwavelet
- 对采集到的电压信号进行小波包分解提取特征向量，再进行BP神经网络训练-On the acquisition of the voltage signal to the wavelet packet decomposition to extract feature vector and then BP neural network training
contourlet_modeling
- Abstract—The contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and direc- tional fi lter banks. The contourlet expansion is composed of basis images oriented at various directions in multiple
MoAT7.1
- This paper identifies a novel feature space to address the problem of human face recognition from still images. This based on the PCA space of the features extracted by a new multiresolution analysis tool called Fast Discrete Curvelet Transfo
xiaobobianhuandejiaotongtuxinagyuchuli
- 基于小波变换的交通图像预处理与特征提取，希望可以帮到大家-Traffic Based on Wavelet Transform Image Pre-processing and feature extraction, the desire to help everyone
attachments_2011_01_25
- wavelet packages used for extracting ECG feature extraction-wavelet packages used for extracting ECG feature extraction...
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- 基于小波包的信号瞬态成分检测与提取方法及其应用,提出基于小波包分解特征表示和瞬态特征重 建方法并应用于汽车变速器齿轮的故障诊断,结果表明基于小波包分解的信号特征表示方法能有效检测信号中瞬 态成分的存在,瞬态成分的重建结果有效地表示了齿轮的故障状态。 -The detection and extraction of signal transients through wavelet packets decomposition are studied and signal trans
fault-diagnosis-of-rolling-bearings
- 滚动轴承故障特征的时间_小波能量谱提取方法，机械工程学报-extraction of rolling bearing fault feature based on time-wavelet energy spectrum,journal of mechanical engineering
wavelet-image-edge-detection
- 方向可调小波图像边缘检测，该算法的主要功能是通过小波变换将图像的边缘检测出来。图像的边缘，按像素点来看应该是灰度值突变的地方，方向可调小波边缘检测正式应用这一特性将图像的边缘提取出来-The steerable wavelet image edge detection, the main function of the algorithm is the edge of the image detected by the wavelet transform. The edge of the ima
Simulation-visual-mechanism
- 提出一个小波域多尺度马尔柯夫随机场模型用于模拟视觉系统在图像分割中的若干功能。针对人类视觉系统具有特征检测器、等级层次性、双向连续性、学习机制等功能，对输入场景，该模型用小波变换提供该场景图像的稀疏表示，模拟特征检测器功能 用金字塔结构模拟等级层次性 用两类信息流模拟双向连接性，分别刻画自底向上的输入图像特征提取过程以及自顶向下的反馈过程 用迭代过程模拟学习机制 采用多尺度马尔柯夫随机场模型实现图像分割。-Put forward a wavelet domain multi-scale mark
Based-onSVM-target-tracking
- 计算Haar小波特征，用AdBaoost提取部分有代表性的特征共三种特征选择方法与SVM相结合进行目标跟踪的算法。 -The calculated Haar wavelet features to extract some of the typical characteristics of three feature selection method combined with SVM algorithm for target tracking AdBaoost.
WignerVille2014
- 本文将小波图像分解和信息熵特征提取相结合，提出一种新的掌纹特征提取算法。该算法首先对掌纹灰度图像进行二维小波分解，再利用多分辨信息熵分别计算不同尺度下的能谱熵作为特征向量，从而实现掌纹特征提取。该算法不但避免了图像增强和纹理细化等预处理过程，而且运用多分辨信息熵的自适应计算方法来调节分解级数，使得到的特征向量长度远小于传统算法。-In this paper, wavelet image decomposition and information entropy feature extractio
Improved-Feature-Extraction-algorithm-Using-WDT.r
- wavelet transform in speech recognition will be briefly reviewed. we describe how the features are extracted, different algorithm, weighting will be introduced-wavelet transform in speech recognition will be briefly reviewed. we describe h
feature-extraction
- EEG feature extraction using wavelet transform
wavelt-and-GrayGradinet
- 小波特征提取和灰度共生矩阵对图线特征进行提取-Wavelet feature extraction and GLCM feature extraction of plot
F4237084615
- Robust Optimal PSO based Wavelet Feature Selection in MIMO OFDM Systems