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利用小波变换进行图像处理的一个源代码，提供给大家学习-wavelet transform an image processing source code available to everyone learning
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基于弹性模板匹配的人脸表情识别程序。首先针对静态表情图像进行表情图像的灰度、尺寸归一化，然后利用Gabor小波变换提取人脸表情特征以构造表情弹性图，最后提出基于弹性模板匹配及K-近邻的分类算法实现人脸表情的识别。-based on elastic template matching Facial Expression identification procedures. First of all expressions against static image looked gray image
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在对原图像进行双正交7／9三级小波分解后 ，在第二级的水平、垂直、对角以及第三级对角分量上加入水印系数，再经过小波反变换重构出图像。-in the original image Biorthogonal 7 / 9 3 wavelet decomposition, in the second-class level, vertical, diagonal, and the third-class right angle to add weight coefficient watermark, a
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In the original image Biorthogonal 7 / 9 3 wavelet decomposition, in the second-class level, vertical, diagonal, and the third-class right angle to add weight coefficient watermark, after wavelet-transform reconstruct images.
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1了解图像变换的意义和手段；
2熟悉离散傅里叶变换、离散余弦变换、离散小波变换的基本性质；
3熟练掌握图像变换的方法及应用；
4通过实验了解二维频谱的分布特点；
5通过本实验掌握利用MATLAB编程实现数字图像的变换。
-An understanding of the meaning and image transformation means 2 familiar with the discrete Fourier transform, discrete cosine t
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采用图像小波变换后的统计特征对图像进行匹配识别-Using wavelet transform the statistical characteristics of image matching recognition
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利用小波变换对图像进行处理，然后识别出车牌号。-Wavelet transform image processing, and brand recognition out of the bus.
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本程序是先用gabor小波变换对人脸图像处理，然后在用pca进行降维，最后用svm分类器进行多分类分类识别，包扩完整的orl人脸库，需注意的是，svm工具箱是用的libsvm工具箱，运行前先配置好libsvm。版本号：libsvm-mat-2[1].89-3[FarutoUltimate3.0]-This procedure is to use the human face gabor wavelet transform image processing, and then to reduce
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小波变换实例。本例中实现了简单的图像识别功能。-Wavelet transform instance. In this case to achieve a simple image recognition.
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基于小波变换和离散余弦变换的图像分级检索算法-Image classification retri algorithm based on wavelet transform and discrete cosine transform
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本案例通过对图像加入高斯噪声，利用sym4小波函数对该图像和信号进行三级分解，用小波阀值去噪的方法对该图像进行去噪，并给出了仿真结果。-This case through join Gaussian noise image, use the sym4 wavelet decomposition of the image and signal, the wavelet threshold to filter method for the image denoising is presented,
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基于小波分析变换提取特征，然后神经网络进行识别(The image recognition is realized, the image feature extraction is realized based on wavelet transform, and then the neural network is trained.)
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