hx_union_adjacent — 2D halcon_ext op

• Data kinds: contour → contour

• Call: fullseye.apply(img, "hx_union_adjacent", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: union_adjacent_contours_xld (the HALCON reference is a useful guide to its meaning and parameters)

hx_union_adjacent: input → output

*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*

Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):

▸ hx_union_adjacent: knob a sweep (docs site)

*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

Stages (the ops that come before → this op, left to right):

▸ hx_union_adjacent: stages (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ hx_union_adjacent: other inputs (docs site)

Usage

Greedily join contours whose end points are close (threshold a).

Scans the list of contours and, if the distance between the end point of cs[i] and the start point of cs[j] (j > i) is at most tol, appends cs[j] to the end of cs[i] to make 1 contour. Each time 1 join is made, the scan restarts from the beginning, repeating until no joinable pair remains.

• a → tolerance tol = 1 + a*8 (1 to 9 pixels).

• b is unused.

Only the "end → start" direction is tested; it does not reverse contours to join when two starts or two ends are close (it assumes a list of consistently oriented contours). Being greedy, when 3 or more contours are candidates it joins with whichever partner is found first in list order (ordering with hx_sort_contours makes the result stable). Join points are not duplicated, and the gap is bridged by a straight jump. The cost is the 2nd power of the number of contours × the number of joins. Check how closed the joined result is with hx_test_closed_xld.

Detailed usage guide

• gallery2d_halcon_ext family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

threshold 0.50 0.50
sk_find_contours 0.50 0.50
hx_union_adjacent 0.50 0.50

▸ Load this pipeline  ·  Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py

Ops the type connects to (they accept contour as input)

identity · select_contours · smooth_contours · fit_line_contours · contours_to_region · count_contours · total_length · select_contours_xld

Same category (halcon_ext)

hx_gen_circle · hx_gen_ellipse · hx_gen_rectangle2 · hx_gen_checker_region · hx_gen_grid_region · hx_gabor · hx_fit_surface1 · hx_fit_surface2


*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.