Class for searching corresponding points on images.
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#include <gdal_simplesurf.h>

static CPLErr  ConvertRGBToLuminosity (GDALRasterBand *red, GDALRasterBand *green, GDALRasterBand *blue, int nXSize, int nYSize, double **padfImg, int nHeight, int nWidth) 
 Convert image with RGB channels to grayscale using "luminosity" method. More...


static CPLErr  MatchFeaturePoints (std::vector< GDALFeaturePoint * > *poMatchPairs, std::vector< GDALFeaturePoint > *poFirstCollect, std::vector< GDALFeaturePoint > *poSecondCollect, double dfThreshold) 
 Find corresponding points (equal points in two collections). More...


Class for searching corresponding points on images.
 Author
 Andrew Migal migal.nosp@m..dre.nosp@m.w@gma.nosp@m.il.c.nosp@m.om
Provides capability for detection feature points and finding equal points on different images. Class implements simplified version of SURF algorithm (Speeded Up Robust Features). As original, this realization is scale invariant, but sensitive to rotation. Images should have similar rotation angles (maximum difference is up to 1015 degrees), otherwise algorithm produces incorrect and very unstable results.
GDALSimpleSURF::GDALSimpleSURF 
( 
int 
nOctaveStart, 


int 
nOctaveEnd 

) 
 
Prepare class according to specified parameters.
Octave numbers affects to amount of detected points and their robustness. Range between bottom and top octaves also affects to required time of detection points (if range is large, algorithm should perform more operations).
 Parameters

nOctaveStart  Number of bottom octave. Octave numbers starts with one 
nOctaveEnd  Number of top octave. Should be equal or greater than OctaveStart 
 Note
 Every octave finds points with specific size. For small images use small octave numbers, for high resolution  large. For 1024x1024 images it's normal to use any octave numbers from range 16. (for example, octave start  1, octave end  3, or octave start  2, octave end  2.) For larger images, try 110 range or even higher. Pay attention that number of detected point decreases quickly per octave for particular image. Algorithm finds more points in case of small octave numbers. If method detects nothing, reduce bottom bound of octave range.
NOTICE that every octave requires time to compute. Use a little range or only one octave if execution time is significant.
Convert image with RGB channels to grayscale using "luminosity" method.
Result is used in SURFbased algorithm, but may be used anywhere where grayscale images with nice contrast are required.
 Parameters

red  Image's red channel 
green  Image's green channel 
blue  Image's blue channel 
nXSize  Width of initial image 
nYSize  Height of initial image 
padfImg  Array for resulting grayscale image 
nHeight  Height of resulting image 
nWidth  Width of resulting image 
 Returns
 CE_None or CE_Failure if error occurs.
Find feature points using specified integral image.
 Parameters

poImg  Integral image to be used 
dfThreshold  Threshold for feature point recognition. Detected feature point will have Hessian value greater than this provided threshold. 
 Note
 Typical threshold's value is 0,001. But this value can be various in each case and depends on image's nature. For example, value can be 0.002 or 0.005. Fill free to experiment with it. If threshold is high, than number of detected feature points is small, and vice versa.
Find corresponding points (equal points in two collections).
 Parameters

poMatchPairs  Resulting collection for matched points 
poFirstCollect  Points on the first image 
poSecondCollect  Points on the second image 
dfThreshold  Value from 0 to 1. Threshold affects to number of matched points. If threshold is lower, amount of corresponding points is larger, and vice versa 
 Returns
 CE_None or CE_Failure if error occurs.
 Parameters

poMatchPairs  Resulting collection for matched points 
poFirstCollect  Points on the first image 
poSecondCollect  Points on the second image 
dfThreshold  Value from 0 to 1. Threshold affects to number of matched points. If threshold is higher, amount of corresponding points is larger, and vice versa 
 Note
 Typical threshold's value is 0,1. BUT it's a very approximate guide. It can be 0,001 or even 1. This threshold provides direct adjustment of point matching. NOTICE that if threshold is lower, matches are more robust and correct, but number of matched points is smaller. Therefore if algorithm performs many false detections and produces bad results, reduce threshold. Otherwise, if algorithm finds nothing, increase threshold.
 Returns
 CE_None or CE_Failure if error occurs.
The documentation for this class was generated from the following files: