nonmax_suppression_dir — Suppress non-maximum points on an edge using a direction image.
nonmax_suppression_dir(ImgAmp, ImgDir : ImageResult : Mode : )
nonmax_suppression_dir suppresses all points in the
regions of the image ImgAmp whose gray values are
not local (directed) maxima. ImgDir is a direction image
giving the direction perpendicular to the local maximum (Unit:
2 degrees, i.e., 50 degrees are coded as 25 in the image). Such
images are returned, for example, by edges_image. Two modes
of operation can be selected:
Each point in the image is tested whether its gray value is a local maximum perpendicular to its direction. In this mode only the two neighbors closest to the given direction are examined. If one of the two gray values is greater than the gray value of the point to be tested, it is suppressed (i.e., removed from the input region. The corresponding gray value remains unchanged).
Like 'nms'. However, the two gray values for the test are obtained by interpolation from four adjacent points.
ImgAmp (input_object) singlechannelimage(-array) → object (byte / uint2 / real)
Amplitude (gradient magnitude) image.
ImgDir (input_object) singlechannelimage(-array) → object (direction)
Direction image.
ImageResult (output_object) image(-array) → object (byte / uint2 / real)
Image with thinned edge regions.
Mode (input_control) string → (string)
Select non-maximum-suppression or interpolating NMS.
Default value: 'nms'
List of values: 'inms', 'nms'
nonmax_suppression_dir returns 2 (H_MSG_TRUE) if all parameters
are correct. The behavior with respect to the input images and
output regions can be determined by setting the values of the flags
'no_object_result', 'empty_region_result', and
'store_empty_region' with set_system.
If necessary, an exception is raised.
edges_image,
sobel_dir,
frei_dir
threshold,
hysteresis_threshold
S.Lanser: “Detektion von Stufenkanten mittels rekursiver Filter nach
Deriche”; Diplomarbeit; Technische Universität München, Institut
für Informatik, Lehrstuhl Prof. Radig; 1991.
J.Canny: “Finding Edges and Lines in Images”; Report, AI-TR-720;
M.I.T. Artificial Intelligence Lab., Cambridge; 1983.
Foundation