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Diffstat (limited to 'macros/detectSURFFeatures.sci')
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diff --git a/macros/detectSURFFeatures.sci b/macros/detectSURFFeatures.sci new file mode 100644 index 0000000..1ee7395 --- /dev/null +++ b/macros/detectSURFFeatures.sci @@ -0,0 +1,45 @@ +function [varargout] = detectSURFFeatures(image, varargin) +// This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image. +// +// Calling Sequence +// result = detectSURFFeatures(Image); +// result = detectSURFFeatures(Image, Name, Value, ...) +// +// Parameters +// result: SURFPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. +// Image : Input image, specified as a A-by-N 2D grayscale. +// MetricThreshold : (Optional) With default value equal to 1000, it is to be specified as a scalar. Every interest point detected has a strength associated with it. In case, only the stronget ones are needed, this parameter has to be given a larger value. To get more no of interest points/blobs, it is to be reduced. +// NumOctaves : (Optional)With default value equal to 3, it is to be specified as a scalar. Larger the number of octaves, larger is the size of blobs detected. This is because higher octave use large sized filters. Value must be an integer scalar in between 1 and 4. +// NumScaleLevels : (Optional)With default value equal to 4, it is to be specified as a scalar. It denotes the number of scale level for each octave. The Value must be an integer scalar greater than or equal to 3. +// ROI : (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. +// Description +// This function return the SURF(Speeded Up Robust Features) Interest Points for a 2D Grayscale image. It is scale- and rotation- invariant point detector and descriptor and its application include Camera Calibration, 3D Reconstruction, Object Recognition to name a few. +// +// Examples +// image = imread('sample.jpg'); +// results = detectSURFFeatures(image); +// +// Authors +// Shashank Shekhar + image_list = mattolist(image); + [ lhs, rhs ] = argn(0) + if rhs > 9 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Not enough input arguments")) + end + select rhs + case 1 then + [a b c d e f] = ocv_detectSURFFeatures(image_list) + case 3 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2)) + case 5 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + case 7 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + case 9 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + varargout(1) = struct('KeyPoints', a, 'Orientation', b, 'Metric', c ,'SignOfLaplacian', d,'Scale', e, 'Count', f ); +endfunction
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