segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "adaptive_gauss_thresh", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: local_threshold (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):
▸ adaptive_gauss_thresh: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ adaptive_gauss_thresh: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ adaptive_gauss_thresh: 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).*
Adaptive thresholding based on a locally Gaussian-smoothed mean. Equivalent to HALCON's local_threshold (Segment an image using local thresholding.).
a sweeps the σ of the Gaussian that forms the reference over 1.0〜4.0, and b the offset over -0.15〜+0.15 ((b-0.5)*0.3). Pixels satisfying v - gaussian_filter(v, σ) > offset become foreground. A perfectly flat surface does not become foreground (even with an offset of 0) —— a pixel becomes foreground only when the difference exceeds 1e-9, so even if the smoothing is off by 1 least-significant bit on a uniform surface, the whole surface does not flip to foreground. On images with uneven illumination it is more stable than a global threshold (_threshold/_otsu). A related op is _dyn_threshold, but it uses a box mean (uniform_filter) as the reference and has a different offset width (±0.2) —— both are an "adaptive threshold", yet they are separate implementations whose reference smoothing method and parameter range differ.
Border handling: mirror repeating the edge pixel (d c b a | a b c d — scipy's default reflect) (measured; tools/impl2/border_probe.py).
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_segmentation 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.
adaptive_gauss_thresh 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
• poc_matrix_code_reading — py -3.11 examples/poc_matrix_code_reading.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
segmentation)threshold · otsu · canny · sk_otsu · sk_li · sk_yen · sk_sauvola · sk_niblack
*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.