hysteresis_threshold
— Perform a hysteresis threshold operation on an image.
hysteresis_threshold(Image : RegionHysteresis : Low, High, MaxLength : )
hysteresis_threshold
performs a hysteresis threshold
operation (introduced by Canny) on an image. All points in the input image
Image
having a gray value larger than or equal to High
are
immediately accepted (“secure” points). Conversely, all points
with gray values less than Low
are immediately rejected.
“Potential” points with gray values between both thresholds are
accepted if they are connected to “secure” points by a path of
“potential” points having a length of at most MaxLength
points. This means that “secure” points influence their
surroundings (hysteresis).
For images of type byte, uint2, or int4 the lower threshold must be
Low
> 0.
Image
(input_object) singlechannelimage(-array) →
object (byte / uint2 / int4 / real)
Input image.
RegionHysteresis
(output_object) region(-array) →
object
Segmented region.
Low
(input_control) number →
(integer / real)
Lower threshold for the gray values.
Default: 30
Suggested values: 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100
High
(input_control) number →
(integer / real)
Upper threshold for the gray values.
Default: 60
Suggested values: 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130
Restriction:
High >= Low
MaxLength
(input_control) integer →
(integer)
Maximum length of a path of “potential” points to reach a “secure” point.
Default: 10
Suggested values: 1, 2, 3, 5, 7, 10, 12, 14, 17, 20, 25, 30, 35, 40, 50
Value range:
1
≤
MaxLength
Minimum increment: 1
Recommended increment: 5
hysteresis_threshold
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.
dyn_threshold
,
threshold
,
class_2dim_sup
,
fast_threshold
edges_image
,
sobel_dir
,
background_seg
J. Canny, “Finding Edges and Lines in Images”; Report, AI-TR-720, M.I.T. Artificial Intelligence Lab., Cambridge, MA, 1983.
Foundation