image =	localEnergy2dImg (inImg,inHalfKnlSizeX,inHalfKnlSizeY)
image =	localEnergy2dImg (inImg,inHalfKnlSizeX,inHalfKnlSizeY,inOptNbClasses)

Detailed Description

local energy on a 2d image

This algorithm computes for each pixel of the output image its associated local energy on a rectangular neighbourhood.

The more homogeneous is the neighbourhood, the higher will be the local energy.

Given an input image $InImg$ , rectangular kernel half sizes $N_x = InHalfKnlSizeX$ and $N_y = InHalfKnlSizeY$ and a number of classes $n$ , output image values are given by :

$OutImg[x, y] = \sum_{i=0}^{n}{p^2(i)}$

where $p(i)$ is the probability deduced from the neighbourhood histogram $H = \left[ h_1, h_2, ..., h_n \right]$ by $p_i = \dfrac {h_{i}} {N_x.N_y}$ .

The histogram is computed on the rectangular kernel, with the following parameters:

number of classes = ,
$min_{histo}$ = minimum value of the whole input image,
$max_{histo}$ = maximum value of the whole input image.

The number of classes is an optional parameter of the algorithm. Its default value equals to 16. If the number of classes specified by the user exceeds the maximum allowed number of classes given the input image data type and dynamic range, it is automatically adjusted. For instance:

a number of classes initialized to value 16 will be automatically adjusted to value 2 for a binary input image,
a number of classes initialized to value 256 will be automatically adjusted to value 10 for a integral input image whose dynamic range equals to .

The borders of the input image are handled by padding pixels with a mirror reflection of the border pixels in the input image (see Border policy for more details).

Here is an example of an output image computed from the LocalEnergy2dImg algorithm on a 8-bits grey level, with a kernel of size 3x3 pixels and 16 classes:

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLStats as stats

Code Example

    # opening of input image
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # local entropy 2d image computation
    outImg = stats.localEnergy2dImg(inImg, 3, 3)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLStats/Processor/LocalEnergy2dImg/LocalEnergy2dImg.h>

Code Example

// compute local energy on input image

ImagePtr pOutImg = localEnergy2dImg(pInImg, inHalfKnlSizeX, inHalfKnlSizeY, nbClasses);