搜索资源列表
WNN
- 小波神经网络及其应用的10片论文 对学习小波神经网络很有用-wavelet neural networks and their application to the 10 theses on wavelet neural network learning useful
Dual_tree_complex_wavelet_transform
- 这是一篇介绍二分树复数小波的文章，里面很详细的介绍了Dual tree complex wavelet transform。希望大家有帮助-two hours on the tree Complex Wavelet article there is a detailed introduction to the Dual tree complex wavelet transform . We hope help
FACE_DETECTION_USING_DT-CWT_ON_SPECTRAL_HISTOGRAM.
- 这是一篇应用二分树复数小波检测人脸的一篇文章，希望与对二分树复数小波感兴趣的人一同切磋-This is the application of two hours a tree Complex Wavelet Face Detection of an article, with the hope that the two sub-tree Complex Wavelet interested in the interaction together
Pattern_recognition_with_SVM_and_dual-tree_complex
- 这是一篇应用二分树复数小波与SVM进行模式识别的一篇文章，希望与对二分树复数小波感兴趣的人一同切磋-This is the application of two hours a tree Complex Wavelet and SVM pattern recognition for an article, with the hope that the two sub-tree Complex Wavelet interested in the interaction together
a_method_of_watermark_by_Dual_tree_complex_wavelet
- 这是一篇应用二分树复数小波进行数字水印的一篇文章，希望与对二分树复数小波感兴趣的人一同切磋-This is the application of two hours a tree Complex Wavelet digital watermark an article, with the hope that the two sub-tree Complex Wavelet interested in the interaction together
Palmprint_Classification_using_Dual-Tree_Complex_W
- 这是一篇应用二分树复数小波进行掌纹分类的文章，希望与共同致力于复数小波的同仁分享-This is an application of two-tree complex wavelet palmprint classification of the article, hope and work together Complex Wavelet colleagues share
Secondgenerationwavelettransform
- 介绍了比较新颖的二代小波变换技术，即通过分裂、预测和更新三个步骤实现小波变换 的方法，并且把这种技术成功的应用于图像融合技术中。实践表明，这是一种比较成功的图像融合解 决方案。
mallatpaper
- Mallat多尺度小波变换图像边缘检测经典文章两篇。Characterization of Signals from Multiscale Edges，Singularity Detection and Processing with Wavelets
EZW
- 1.基于EZW的图像编码改进算法 2.基于EZW的嵌入式图像编码算法的研究 3.改进的嵌入小波算法在遥感图像压缩中的应用-1. EZW-based image coding to improve the algorithm 2. EZW-based embedded image coding algorithm 3. Improved embedded wavelet image compression algorithms in remote sensing application
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
log-omaicircuits
- 模拟小波基的构造及其对数域电路实现与应用研究-Simulation of the structure of wavelet and its log-domain circuits and applied research
SuperResolutionBlindReconstructionofLowResolutionI
- 基于小波变换的图像超分辨率重建资料，比较有参加考价值-SuperResolutionBlindReconstructionofLowResolutionImagesusingWaveletsbasedFusion
xiaobosjinwangl
- 利用多分辨分析方法,结合小波分析和神经网络思想构建一种新型的神经网络模型———小波神经网络,解决了传 统神经网络中隐层节点数难以确定的问题。通过对股票的预测,说明该方法能有效地提高预测精度, 避免了人工神经网 络模型的固有缺陷。 -Using multi-resolution analysis method, combined with wavelet analysis and neural network ideological construct a new neural net
1000-2375(2008)03-0245-04
- BBB基于小波分解、互信息测度以及混合优化的图像配准-BBB based on wavelet decomposition, mutual information measure and the hybrid optimization of image registration
wavelets-
- 小波分解被应用在众多领域，这篇文档是从网上摘录的资料，可帮助初学者利用matlab快速试难小波分解的用法和特点-Usage and characteristics of the wavelet decomposition be applied in many fields, this document is an excerpt from the online information that can help beginners quickly try hard wavelet decompo
小波变换
- 这是一个关于小波变换的ppt讲解，说的比较透彻，可以看一下
Wavelet-transform
- 针对滚动轴承信号的特点，构造脉冲响应小波，采用连续小波变换的方法提取滚动轴承故障信号，提出两种诊断方法。-For Rolling signal characteristics and tectonic pulse response wavelet using continuous wavelet transform to extract the rolling bearing fault signals, and proposed two diagnostic methods.
cg09000773
- 基于小波变换的图像纹理特征提取方法及其应用-A Method of Image Texture Feature Analysis Based on Wavelet Decomposition and its Application
QYXXHJC
- 变频器高次谐波产生的奇异性信号使交流调速系统不能正常工作, 采用 小波包分析对奇异性信号进行识别和提取, 对系统进行抗干扰处理, 有效地抑制了高次 谐波对交流调速系统的干扰。-H igh o rder harm on ic o f inverter produces singularity s igna ,l m ake speed governor system can. t no rm a lly ope rate. W avelet packe t theoryw as app
removal-artifacts--EEG-
- 通过小波分析和独立分量分析来去除脑电信号中的运动伪迹，有很好的参考价值。-Wavelet analysis and independent component analysis to remove EEG motion artifact, a good reference value.(Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and