halcon_ext op• Data kinds: image → region
• Call: fullseye.apply(img, "hx_detect_edge_segments", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: detect_edge_segments (the HALCON reference is a useful guide to its meaning and parameters)
*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_detect_edge_segments: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ hx_detect_edge_segments: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ hx_detect_edge_segments: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Detect straight edge fragments: thin by NMS, then keep the connected components that PCA finds elongated (line-like).
Splits the edges thinned by hx_nonmax_dir(v, a, 0) (> 0) into connected components with the 8-neighbourhood, and returns a 0/1 float region in which only components whose covariance eigenvalue ratio of pixel coordinates λmax/λmin is at least min_ratio (elongated = line-like) are 1.
• a → weak-edge threshold of the thinning stage (same as a of hx_nonmax_dir; normalised amplitudes below a*0.3 are discarded).
• b → elongation threshold min_ratio = 3 + b*12 (3 to 15). The larger, the more only straight fragments remain.
• Components with fewer than 5 pixels are discarded unconditionally. If λmin is nearly 0 (a perfect line), the ratio is taken as infinite and the component is adopted.
Caution: the linearity test is "whether the spread of the point cloud is biased toward 1 axis", so even curves pass if they are elongated enough. Because the thinning stage hardly thins diagonal edges (see the notes of hx_nonmax_dir), diagonal edges become thick blobs whose eigenvalue ratio drops, and they tend to be dropped. Since it works per connected component, crossing lines form 1 component and the ratio drops. To handle each line segment downstream, use hx_region_to_label or hough_line_trans.
Blank frame (measured): feeding an image of uniform brightness gives an empty result — every pixel is background (0) — regardless of the brightness. Pure white and pure black behave alike; brightness by itself detects nothing.
• gallery2d_halcon_ext family guide
• 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.
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.
hx_detect_edge_segments 0.50 0.50
▸ Load this pipeline · Load & run
• find_ops_by_meaning — py -3.11 examples/find_ops_by_meaning.py
• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
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.