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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
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