pfisher

Performs multi-thresholding on image using Fisher algorithm.

Synopsis

pfisher minval nbclass [-m mask] [im_in|-] [im_out|-]

Description

pfisher classifies the input image pixels into a small number of clusters according to their value. Every pixel p of the input image is assigned to a cluster identified by the related threshold value:

   if threshold[k-1]<im_out[p]<=threshold[k].
   then im_out[p]=threshold[k]

The last threshold is equal to 255.

The classification is based on the histogram partitioning into nbclass distinct classes so as to minimize the sum of the class variance.

Notice: This operator can only work on grayscale image of bytes.

Parameters

minval is the minimum gray level from which the histogram is built. The value is generally 0 but can be used to mask some gray levels.
nbclass specifies the number of class. It is an integer from [2 .. 25].

Inputs

im_in: a grayscale image of bytes (Img2duc, Img3duc).

Outputs

im_out: a grayscale image of bytes (Img2duc, Img3duc).

Result

Returns SUCCESS or FAILURE.

Examples

Segments the tangram pieces:

   pfisher 0 2 tangram.pan out.pan

C++ prototype

Errc PFisher( const Img2duc &im_in, Img2duc &im_out, Uchar minval, int nbclass );

Version française

Multiseuillage de l'image par partionnement de l'histogramme des niveaux de gris.

Author: Régis Clouard