local_min_sub_pix — Subpixel precise detection of local minima in an image.
local_min_sub_pix extracts local minima from the image Image with subpixel precision. To do so, in each point the input image is approximated by a quadratic polynomial in x and y and subsequently the polynomial is examined for local minima. The partial derivatives, which are necessary for setting up the polynomial, are calculated either with various Gaussian derivatives or using the facet model, depending on Filter. In the first case, Sigma determines the size of the Gaussian kernels, while in the second case, before being processed the input image is smoothed by a Gaussian whose size is determined by Sigma. Therefore, 'facet' results in a faster extraction at the expense of slightly less accurate results. A point is accepted to be a local minimum if both eigenvalues of the Hessian matrix are greater than Threshold. The eigenvalues correspond to the curvature of the gray value surface.
Method for the calculation of the partial derivatives.
Default value: 'facet'
List of values: 'facet', 'gauss'
Suggested values: 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 3.0
Restriction: Sigma >= 0.0
Minimum absolute value of the eigenvalues of the Hessian matrix.
Default value: 5.0
Suggested values: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0
Restriction: Threshold >= 0.0
Row coordinates of the detected minima.
Column coordinates of the detected minima.
local_min_sub_pix returns 2 (H_MSG_TRUE) if all parameters are correct and no error occurs during the execution. If the input is empty the behavior can be set via set_system('no_object_result',<Result>). If necessary, an exception is raised.
critical_points_sub_pix, local_max_sub_pix, saddle_points_sub_pix
local_min, lowlands, lowlands_center