Features2dGreySignature =	extractGreySignedFeatures2d (inImg,inNbFeatures)
Features2dGreySignature =	extractGreySignedFeatures2d (inImg,inGradStdDev,inNbFeatures,inFeaturesDist)
Features2dGreySignature =	extractGreySignedFeatures2d (inImg,inGradStdDev,inOptGradientGaussianCoverage,inCornerDetectionParams,inNbFeatures,inFeaturesDistX,inFeaturesDistY,inInterpolationPolicy,inSamplingBallInfo2d)

Detailed Description

algorithm allowing to extract 2d features associated to a grey signature from an image

This algorithm combines a feature detector algorithm (see Harris corner detection 2d) with a grey level feature signature algorithm allowing description of neighborhood of detected points.

Feature detector is controlled by following parameters : $InOptGradStdDev$ , $InOptGradientGaussianCoverage$ , $InOptCornerDetectionParams2d$ , $InNbFeatures$ , $InOptFeaturesDistX$ and $InOptFeaturesDistY$ . See Harris corner detection 2d for more informations on these parameters.

Local feature signature is computed using a "sampling ball" which is composed of rings centered at feature point. Each ring is associated to a radius, to a blurring factor (we sample blurred versions of input image $InImg$ using Gaussian Smoothing 2d algorithm) and to a number of samples regularly dispatched around sampling ring. Angle of first sample $\overrightarrow{V}$ is defined using orientation of inertia tensor main direction. Note that blurr factor usually increase with sampling ring radius.

Sampling ball can be customized via parameter $InOptSamplingBallInfo2d$ . All extracted values are stacked into a single "by plane" data collection $OutPIGreySignatures2d$ .

Parameter $InOptInterpolationPolicy$ allows to specify an interpolation policy used during signature sampling.

An optional scale factor $InOptSignatureScaleFactor$ allows to generate comparable feature signatures for images with different scale factors (results of this algorithm are not scale invariant).

This algorithm is based on an adaptation of following work :

Nicolas Allezard, Michel Dhome, Frederic Jurie. Mise en correspondance multi-echelle. 17ieme Colloque sur le Traitement du Signal et des Images, Sep 1999, Vannes, France. Groupe d'Etudes du Traitement du Signal et des Images (GRETSI), pp.897–900, 1999.

See also: https://en.wikipedia.org/wiki/Feature_detection_(computer_vision)

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLRegistration as registration

Code Example

    # opening of input image
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    nbFeatures = 100
    
    # extraction of grey signed features from image
    outGreySignatures1 = registration.extractGreySignedFeatures2d(inImg, nbFeatures)
    print("First feature found at {x=" + str(outGreySignatures1.signatureColl[0].x) +
                                ", y=" + str(outGreySignatures1.signatureColl[0].y) + "}")

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLRegistration/Processor/ExtractGreySignedFeatures2d/ExtractGreySignedFeatures2d.h>

Code Example

    // Load the input image
    ImagePtr pInImg = loadTiffImageFile(inImgFilePath);
    // extraction of grey signed features from image
    Features2dGreySignaturePtr pOutGreySignatures1 = extractGreySignedFeatures2d(pInImg, 100);