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hog

于 2013-10-19 发布 文件大小:137KB
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  方向梯度直方图的全部代码,广泛应用于行人检测行人检测(Histogram of Oriented Gradients code Pedestrian Detection)

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cell_Hog.m,1328,2013-08-18

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    基于区域的空间域图像融合。先对源图像作小波分解,低频分量加权平均,高频分量用模糊C均值聚类算法进行区域分割,对区域进行基于ssim值的融合,最后小波逆变换得到融合图像。(region based spatial domain method.First do the source image wavelet decomposition low frequency components weighted average of the high frequency component using Fuzzy C-means clustering algorithm region segmentation of regional integration based ssim value, and finally fused image inverse wavelet transform)
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    2007-11-08 20:26:44下载
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  • GMM高斯混合模型进行背景建模(Matlab)
    转:https://blog.csdn.net/jinshengtao/article/details/26278725 混合高斯背景建模是基于像素样本统计信息的背景表示方法,利用像素在较长时间内大量样本值的概率密度等统计信息(如模式数量、每个模式的均值和标准差)表示背景,然后使用统计差分(如3σ原则)进行目标像素判断,可以对复杂动态背景进行建模,计算量较大。 在混合高斯背景模型中,认为像素之间的颜色信息互不相关,对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点,其值在序列图像中的变化可看作是不断产生像素值的随机过程,即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰),多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak).)
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    2016-08-30 16:13:14下载
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    说明:  dctcom.m文件利用DCT变换完成对输入图像进行压缩;imagecbe.m完成对输入的两幅RGB图像用小波分析的方法进行图像融合 imagecom.m完成对输入的RGB图像用小波分析的方法进行自动降噪,得到高频系数阈值,降噪效果百分比和结果 wavelet1D.m完成对输入的一维信号进行多尺度离散小波分解 wavelet2D完成对输入的二维信号进行多尺度离散小波分解;zigzag.m完成对输入的8*8矩阵按照zigzag排列抽取数据.(document the use of DCT transform dctcom.m completion of the input image compression imagecbe.m completion of the two RGB input image with wavelet analysis method of image fusion imagecom.m completion of the input RGB image using wavelet analysis method automatically drop noise, the threshold of high-frequency coefficient, noise reduction effect and the results of the percentage of wavelet1D.m completion of the input signals of one-dimensional discrete wavelet multi-scale decomposition wavelet2D completion of the input signals of two-dimensional discrete wavelet multi-scale decomposition zigzag.m completed the input of 8* 8 matrix of data collected in accordance with the zigzag order.)
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