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于 2017-12-07 发布 文件大小:45KB
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说明:  利用基于直方图的自适应阈值方法实现分割前景与背景a)设定三个不同阈值,直接观察分割结果; b) 利用统计直方图,得到一个自适应的阈值.(The segmentation foreground and background a are realized by using the histogram based adaptive threshold method, and three different thresholds are set, and the segmentation results are observed directly. B) a adaptive threshold is obtained by using the statistical histogram.)

文件列表:

1\Histogram_based_segmentation.m, 3351, 2017-12-02
1\Test_Img_1.jpg, 18649, 2013-11-01
1\Test_Img_2.jpg, 23072, 2013-11-01
1\Test_Img_3.jpg, 28704, 2013-11-01
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    通过阈值去噪,最关键的就是要确定最佳阈值T 的大小.如果阈值太小,可以保留更多的小波系数,但同时噪声也被保留;相反,阈值太大,可以消除更多的噪声,但重要图像特征又将被滤掉,引起偏差.目前使用的阈值分为全局阈值和局部阈值.全局阈值对各层所有的小波系数或同一层内的小波系数都是统一的;局部阈值则是根据当前系数周围的局部情况来确定合适的阈值,具有灵活性.(Threshold denoising, the most critical is to determine the optimal threshold T s size. If the threshold is too small, you can retain more of the wavelet coefficients, but the noise was retained the contrary, the threshold is too large, can be eliminatedmore noise, but the important image features in turn be filtered out, causing bias current threshold is divided into a global threshold and local threshold. global Threshold the wavelet coefficients of all the wavelet coefficients of the layers or on the same floorare unity local threshold is based on the local situation of the current coefficient around to determine the appropriate threshold, with flexibility.)
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