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| author | shamikam | 2017-01-16 02:56:17 +0530 |
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| committer | shamikam | 2017-01-16 02:56:17 +0530 |
| commit | a6df67e8bcd5159cde27556f4f6a315f8dc2215f (patch) | |
| tree | e806e966b06a53388fb300d89534354b222c2cad /macros/detectMinEigenFeatures.sci | |
| download | FOSSEE_Image_Processing_Toolbox-a6df67e8bcd5159cde27556f4f6a315f8dc2215f.tar.gz FOSSEE_Image_Processing_Toolbox-a6df67e8bcd5159cde27556f4f6a315f8dc2215f.tar.bz2 FOSSEE_Image_Processing_Toolbox-a6df67e8bcd5159cde27556f4f6a315f8dc2215f.zip | |
Diffstat (limited to 'macros/detectMinEigenFeatures.sci')
| -rw-r--r-- | macros/detectMinEigenFeatures.sci | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/macros/detectMinEigenFeatures.sci b/macros/detectMinEigenFeatures.sci new file mode 100644 index 0000000..e6b4485 --- /dev/null +++ b/macros/detectMinEigenFeatures.sci @@ -0,0 +1,50 @@ +function [cornerPoints]=detectMinEigenFeatures(image,varargin) +// This function is used to find corner points in an image using Minimum Eigen Value algorithm. +// +// Calling Sequence +// points = detectMinEigenFeatures(I); +// points = detectMinEigenFeatures(I, Name, Value, ...); +// +// Parameters +// points: Structure of corner points +// I: Input image to detectHarrisFeatures() +// MinQuality: (Optional) Minimum accepted quality of corners (Default- 0.01) +// FilterSize: (Optional) Dimension of Gaussian Filter (Default: 5) +// ROI: (Optional) Rectangular region for corner detection +// +// Description +// This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image. +// +// Examples +// I = imread('sample.jpg'); +// points = detectMinEigenFeatures(I); +// +// Authors +// Rohit Suri +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs>7 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)==0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location metric count]=opencv_detectMinEigenFeaturess(imageList); + case 2 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + end + //disp(count(1,1)); + cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); + //for i=1:count(1,1) + // cornerPoints(i)=struct('Type','cornerPoints','Location',location(i,:),'Metric',metric(i,:),'Count',1); + //end +endfunction |