资源列表
ARM
- 虹膜识别虹膜特征提取两种比较典型的方法— 多通道滤波 二维小波变换 -Iris Recognition Iris feature extraction two kinds of typical methods- multi-channel two-dimensional wavelet transform filter
tezhengtiqu
- 基于小波变换的特征提取，并且给出了利用小波变换的纹理特征提取和颜色特征提取。-Feature extraction based on wavelet transform and wavelet transform are given texture and color feature extraction feature extraction.
matlab
- 基于matlab的DCT变换做的图像压缩处理，一些简单的处理-The DCT-based matlab to do image compression, some simple processing
Wavelet-algorithm
- 小波分析诞生于20世纪80年代, 被认为是调和分析即现代Fourier分析发展的一个崭新阶段。众多高新技术以数学为基础,而小波分析被誉为“数学显微镜”,这就决定了它在高科技研究领域重要的地位。目前, 它在模式识别、图像处理、语音处理、故障诊断、地球物理勘探、分形理论、空气动力学与流体力学上的应用都得到了广泛深入的研究,甚至在金融、证券、股票等社会科学方面都有小波分析的应用研究-Wavelet analysis was born in the 1980s, is considered the mo
hough3
- 利用Hough变换实现直线的快速精确检测 利用Hough变换对直线进行检测,通常存在“速度缓慢、结果不够精确”的问题,确检测成为可能。-Using Hough transform line detection using Hough transform fast and accurate detection of a straight line, usually there is " slow, the result is not precise enough" issue, this pap
hough4
- 一种基于随机Hough 变换圆检测的改进算法广义的 Hough 变换可以推广至检验任意形状。-Based on randomized Hough transform circle detection algorithm improved generalized Hough transform can be extended to test any shape.
hough5
- 提出了一种基于小波和Hough 变换的仿射不变性商标检索方法. 根据商标图像色彩调、纹理简单的特点, 首先对商标图像进行二值化处理并做Hough 变换 -Proposed a Hough transform based on wavelet and affine invariance trademark retrieval method according to trademark the image color tone, texture characteristics of a simple
QuickWaveletTransform2011Algo
- 2011 Quick Improved Wavelet Transform Algorithm
gab
- 使用二维gabor滤波器提取图像的纹理特征，并使能量的平均值为其特征值-extricate the image texture feature
Wavelet_denoising
- 基于小波变换的纹理特征提取方法，可以提取特征值，经过修改也可以提取其他特征向量！-Wavelet-based texture feature extraction method can extract the characteristic values can also be modified to extract the other eigenvectors!
wavelet_alias
- 基本小波变换，正变换之后丢掉一部分系数后的混叠性分析和演示-Basic wavelet transform, after losing part of the transformation coefficients are after aliasing analysis and presentation
wavesh_in_2D
- 时域信号的平移引起小波变换系数的平移之演示程序-Time-domain signal caused by the translation of the translation of the wavelet coefficients demo