pchanda

Synopsis

Description

pchanda 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 number of clusters and the value of the thresholds are determined from the Chanda et al. algorithm. It is based on the measure of the average contrast value. For each gray level i the average contrast value is:

	         SUM(SUM((Tkl)*(Tkl))   SUM(SUM((Tpq * Tpq)))
   contrast(i) = -------------------- + ---------------------
	         SUM(SUM(Tkl))          SUM(SUM(Tpq))
		

with k in [0..i], l in [i+1..N-1], p in [i+1..N-1] and q in [0..i].

The co-occurrence matrix Tkl contains the number of times the gray level k is the neighbor of the gray level l considering the neighborhood Nxy={ (x,y+1) }.

Then the thresholds are located as regional maxima of the average contrast function. The maxima are searched in the space of length gray levels around the gray level i.

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

Parameters

• length defined the length of the search space of the regional maxima. It is defined in gray level unit. The greater is the length, the less there are thresholds. A typical value is 10.

Inputs

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

Outputs

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

Result

Returns the number of thresholds.

Examples

Segments tangram.pan and displays the number of thresholds:

   pchanda 20 tangram.pan out.pan
   pstatus

See also

C++ prototype

Version française

Multiseuillage de l'image par analyse de la matrice de co-occurences selon Chanda.

Reference

B. Chanda, Chauduri and Majumder, "On Image Enhancement and threshold selection using the gray lavel co-occurence matrix", Pattern Recognition Letter, Vol.3, No. 4, pp. 243-251, 1985.