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matlab MRF toy examples

于 2017-07-09 发布 文件大小:21KB
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下载积分: 1 下载次数: 8

代码说明:

  关于马尔科夫随机场的分割matlab代码(On the Markov random field segmentation matlab code)

文件列表:

matlab MRF toy examples
matlab MRF toy examples\addNoise.m
matlab MRF toy examples\computeBeliefs.m
matlab MRF toy examples\gaussplot.m
matlab MRF toy examples\getCliqueProb.m
matlab MRF toy examples\getLogProb.m
matlab MRF toy examples\getPropMat.m
matlab MRF toy examples\getRasterNeighbors.m
matlab MRF toy examples\ICM.m
matlab MRF toy examples\initBPMessages.m
matlab MRF toy examples\initICM.m
matlab MRF toy examples\initLocalEvidence.m
matlab MRF toy examples\initMRF.m
matlab MRF toy examples\initNodes.m
matlab MRF toy examples\initPaths.m
matlab MRF toy examples\ipf.m
matlab MRF toy examples\marginals2image.m
matlab MRF toy examples\multMessages.m
matlab MRF toy examples\oneIterBP.m
matlab MRF toy examples\runBP.m
matlab MRF toy examples\showIm.m
matlab MRF toy examples\states2image.m
matlab MRF toy examples\testMRF.m
matlab MRF toy examples\utAxisAngleToRotmat.m
matlab MRF toy examples\utCropImageToPowerOfTwo.m
matlab MRF toy examples\utFilename.m
matlab MRF toy examples\utIf.m
matlab MRF toy examples\utIsScalarBoolean.m
matlab MRF toy examples\utIsScalarInteger.m
matlab MRF toy examples\utIsScalarReal.m
matlab MRF toy examples\utIsSingleString.m
matlab MRF toy examples\utNormalize.m
matlab MRF toy examples\utParseArgs.m
matlab MRF toy examples\utReadPnm.m
matlab MRF toy examples\utRotmatToAxisAngle.m
matlab MRF toy examples\utSafeInverse.m
matlab MRF toy examples\utSafeMult.m
matlab MRF toy examples\utSigmoid.m
matlab MRF toy examples\utWritePnm.m

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    积分:1
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    说明:  小波分析增强滤波。小波变换的原理类似傅里叶变换,只是把三角函数基换成了小波基。与傅里叶变换不同,小波变换有两个变量:scale和translation。scale控制小波函数的收缩,其导数即为频率,translation控制小标函数的平移,平移量对应时间。通过信号的伸缩平移,可以得到某种重合情况,这样积分也会得到一个极大值,不同的是,得到频率成分的同时,还可以知道该频率的时间位置。(Wavelet analysis enhances filtering.The wavelet transform works like the Fourier transform, except that the trigonometric basis is replaced by a wavelet basis. Unlike the Fourier transform, the wavelet transform has two variables: scale and Translation. Scale controls the contraction of the wavelet function, its derivative is frequency, translation controls the translation of the small scale function, and the translation corresponds to the time. By stretching and shifting the signal, you can get some kind of coincidence, so that the integral will also get a maximum, but the difference is that when you get the frequency component, you can also know the time position of that frequency.)
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    本程序和实现对一幅包含人脸的bmp图像进行光纤补偿,对人脸区域进行检测实现人眼和人嘴的检测与定位,最后勾绘出人脸的特征以实现人脸检测的目的。(This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finally drew out the facial features to achieve the purpose of face detection. )
    2013-04-17 10:37:31下载
    积分:1
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