monotony🔗
Short description🔗
monotony — Calculating the monotony operation.
Signature🔗
monotony( image Image, out image ImageMonotony )
Description🔗
The operator monotony calculates the monotony operator.
Thereby the points which are strictly smaller than the current
gray value will be counted in the 8 neighborhood. This number
will be entered into the output imaged.
If there is a strict maximum, the value 8 is returned; in case of a minimum or a plateau, the value 0 will be returned. A ridge or a slope will return the corresponding intermediate values.
The monotony operator is often used to prepare matching operations as it is invariant with regard to lightness modifications.
Attention🔗
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Execution information🔗
Execution information
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Multithreading type: reentrant (runs in parallel with non-exclusive operators).
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Multithreading scope: global (may be called from any thread).
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Automatically parallelized on tuple level.
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Automatically parallelized on channel level.
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Automatically parallelized on domain level.
Parameters🔗
Image (input_object) (multichannel-)image(-array) → object (byte / int2 / uint2)
Input image.
ImageMonotony (output_object) (multichannel-)image(-array) → object (byte / int2 / uint2)
Result of the monotony operator.
Number of elements: ImageMonotony == Image
Example🔗
(C)
/* searching the strict maximums */
gauss_filter(Image,&Gauss,5)\;
monotony(Gauss,&Monotony)\;
threshold(Monotony,Maxima,8.0,8.0)\;
Combinations with other operators🔗
Combinations
Possible predecessors
binomial_filter, gauss_filter, median_image, mean_image, smooth_image, invert_image
Possible successors
threshold, exhaustive_match, disp_image
Alternatives
Module🔗
Foundation