Classify pixels using hyper-cuboids.
class_ndim2__ classifies the pixels of the multi-channel image given in MultiChannelImage. To do so, the current classifier created with create_classif2 is used. The classifier can be trained using learn_ndim2__ or as described with create_classif2. More information on the structure of the classifier can be found also under that operator.
MultiChannelImage (input_object) |
multichannel-image(-array) -> object : byte / int4 / real |
Multi-channel input image. |
Regions (output_object) |
region-array -> object |
Segmentation result. |
NumberOfChannels (input_control) |
integer -> integer |
Number of gray channels. | |
Default value: 3 | |
Suggested values: 1, 2, 3, 4, 5, 6, 7, 8 | |
Range of values: 1 <= NumberOfChannels | |
Minimum increment: 1 |
read_image(:Bild:'meer':) > disp_image(:Image::) > set_color(::'green':) > fwrite_string(::'Draw the learning region':) > fnew_line(:::) > draw_region(:Reg1::) > reduce_domain(Image,Reg1:Foreground::) > set_color(::'red':) > fwrite_string(::'Draw Background':) > fnew_line(:::) > draw_region(:Reg2::) > reduce_domain(Image,Reg2:Background::) > fwrite_string(::'Start to learn'::) > fnew_line(:::) > create_classif(::'meer_farbe':) > learn_ndim2__(Foreground,Background,Image::3:) > fwrite_string(::'start to classificate':) > fnew_line(:::) > class_ndim2__(Image:Res:3:) > set_draw(::'fill':) > disp_region(Res:::) > free_classif(:::).
Let N be the number of hyper-cuboids and F be the area of the input region. Then the runtime complexity is O(N * F).
class_ndim2__ returns TRUE if all parameters are correct. The behaviour 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.
create_classif2, learn_classif2, median, compose2, compose3, compose4
learn_ndim2__, descript_classif2, create_classif2