Subpixel precise detection of critical points in an image.

critical_points_sub_pix extracts critical points, i.e., local maxima, local minima, and saddle points, 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 extremal values and saddle points. 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 critical point if the absolute values of both eigenvalues of the Hessian matrix are greater than Threshold. The eigenvalues correspond to the curvature of the gray value surface. If both eigenvalues are negative, the point is a local maximum, if both are positive, a local minimum, and if they have different signs, a saddle point.

critical_points_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 handling is raised.

critical_points_sub_pix is reentrant and processed without parallelization.

gen_cross_contour_xld, disp_cross

local_min_sub_pix, local_max_sub_pix, saddle_points_sub_pix

local_min, local_max, plateaus, plateaus_center, lowlands, lowlands_center

Foundation