Max Propagation 2d

image =

maxPropagation2dImg (inImg)

Detailed Description

propagation of maxima in a 2d image

Maxima propagation algorithm allows to propagate maximum grayscale values inside particules separated by zero grayscale pixel values with respect to a given neighborhood 2d policy (see 2d neighborhood models).

Two versions of this algorithm are implemented which can be selected using attribute InOptOptimizationPolicy which associated to enumerate ipsdk::imaproc::attr::eProcessingOptimizationPolicy :

• a 'fast' version using a binary and a label intermediate image (which can optionally be provided via OutOptWk1BinImg and OutOptWk1LabelImg attributes)
• a low memory consumption iterative algorithm which propagate maxima until convergence.

The following figure illustrate algorithm effect on synthetic data :

Here is an example of maxima propagation applied to a grayscale input image (with 8 connexity neighborhood type) :

Example of Python code :

Example imports

import PyIPSDK
import PyIPSDK.IPSDKIPLAdvancedMorphology as advmorpho

Code Example

    # opening of input image
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # maxima 2d propagation computation
    outImg = advmorpho.maxPropagation2dImg(inImg)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLAdvancedMorphology/Processor/MaxPropagation2dImg/MaxPropagation2dImg.h>

Code Example

        // opening input image
        ImagePtr pInImg = loadTiffImageFile(inputImgPath);

        // compute maximum propagation on input image
        ImagePtr pOutImg = maxPropagation2dImg(pInImg);