Extract regions with equal gray values from an image.

label_to_region segments an image into regions of equal gray value. One output region is generated for each gray value occuring in the image. This is similar to calling threshold multiple times, and accumulating the results with concat_obj. Another related operator is regiongrowing. However, label_to_region does not perform a connection operation on the resulting regions, i.e., they may be disconnected. A typical application of label_to_region is the segmentation of label images, hence its name.

The number of output regions is limited by the system parameter 'max_outp_obj_par', which can be read via

get_system(::'max_outp_obj_par':<Anzahl>) .

label_to_region is not implemented for images of type 'real'. The input images must not contain negative gray values.

Let x1 be the minimum x-coordinate, x2 the maximum x-coordinate, y1 be the minimum y-coordinate, and y2 the maximum y-coordinate of a particular gray value. Furthermore, let N be the number of different gray values in the image. Then the runtime complexity is O(N * (x2 - x1 + 1) * (y2 - y1 + 1))

label_to_region returns 2 (H_MSG_TRUE) if the gray values lie within a correct range. 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.

label_to_region is reentrant and automatically parallelized (on tuple level).

min_max_gray, sobel_amp, gauss_image, reduce_domain, diff_of_gauss

connection, dilation1, erosion1, opening, closing, rank_region, shape_trans, skeleton

threshold, concat_obj, regiongrowing, region_to_label

Foundation