搜索资源列表
Researchontheshapefeatureextractionandrecognition.
- 主分量分析(PCA ) 是统计学中分析数据的一种有效的方法, 可以将数据从高维数据空间变换到低维特征空间, 因而 可以用于数据的特征提取及压缩等方面。在该文的形状识别系统中, 用PCA 法提取图像的形状特征, 能够较好地满足识别 层的输入要求。在识别层研究了3 种识别方法: 最近邻法则、BP 网络及协同神经网络方法, 均取得了满意的实验效果。-Principal component analysis (PCA) is a statistical analysis of data in a
IMG_PCA
- Matlab 实现 一维 PCA 压缩解压 图像,可以设置 eigen value 数目,观看压缩结果。-Matlab to achieve one-dimensional PCA-extracting compressed images, the number of eigen value can be set to watch the results of compression.
23825772hyperspectral
- 高光谱图像的一系列处理包括了例如融合压缩pca变换等等等等的程序。-Hyperspectral image processing includes a series of transformations such as the integration of compression pca, etc., etc. procedures.
PCA_cov
- 用神经网络中的PCA算法对人脸图像进行压缩及恢复-PCA with a neural network algorithm for image compression on the face and back
dsp_project
- the code conducts the image compression of the gray scale image up to 90 using 4 algos fft wavelet pca and cosine transform-the code conducts the image compression of the gray scale image up to 90 using 4 algos fft wavelet pca and cosine transform
pca-and-wavelet-for-feature
- 结合PCA和Wavelet进行图像压缩和特征提取等方面的研究-fuse wavelet and PCA for image compression,denoise and feature extraction
face-recognitionnn
- The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. PCA is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the la
GeoBlur
- Geoblur PCA. good for video compression.
image-processing
- 内有操作说明: 操作说明.docx 加噪:高斯、椒盐等 去噪:小波、高斯、维纳及频域上的滤波(频率可调) 压缩:JPEG、小波、PCA、位平面、FFT、DCT 以上功能都集成在了GUI界面内-There are instructions: instructions docx plus noise: Gaussian, salt and pepper and other de-noising: wavelet, Gaussian, and Wiener filter in the
histeq
- 主成分分析算法(PCA),可用于降维,也可用于处理图像相关性问题,提取主成分,分析图像细节信息和主要成分,用于图像压缩也可以-Principal component analysis algorithm (PCA), can be used for dimensionality reduction, can also be used to process images related issues, extracted principal component analysis and main
