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Be the first to rate this file! 2 Downloads (last 30 days) File Size: 2.45 KB File ID: #30480 Version: 1.0
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Dirk-Jan Kroon (view profile)


Smooth histograms for sparse sampled signals and images, using low-frequency assumption.

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This Function HistConnect will make a smooth histogram for a signal or image consisting of a few samples. The function assumes correlation between the samples, and implicitly generates new linear-interpolated samples between the originate samples. Thus makes the assumption of continuity between samples and low-frequency signal.
note : If your samples are spatial uncorrelated, use X=sort(X) before using this function
   X : A 1D vector or 2D matrix (image) with sample values
   B : The number of histogram bins (default 256)
   R : A vector [1 x 2], with the min and max histogram boundary,
                    (default R=getrangefromclass(X))
  Example how it works,
   - You want 3 bins
   - Measured samples [0 0.3 0.7 1]
   - Histogram edges [0 1/3] [1/3 2/3] [2/3 1];
   A normal histogram function will return:
    H= [2 0 2]
   This histogram function makes histogram-blocks between two
   values in the sample vector
     sample value 0 connect with 0.3 : [1.33 0.00 0.00]
     sample value 0.3 connect with 0.7 : [0.11 1.11 0.11]
     sample value 0.7 connect with 1.0 : [0.00 0.00 1.33]
                    H = [1.44 1.11 1.44]

MATLAB release MATLAB 7.11 (R2010b)
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