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