segmentation op• Data kinds: image → region
• Call: fullseye.apply(img, "cv_adaptive_mean", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: dyn_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):
▸ cv_adaptive_mean: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ cv_adaptive_mean: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ cv_adaptive_mean: 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).*
Thresholding based on the local mean (adaptive threshold, mean variant, OpenCV implementation). Uses, as the threshold, the simple average of each pixel's neighborhood minus a constant - more stable than a global threshold on images with uneven illumination.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON の dyn_threshold(Segment an image using a local threshold.)に相当。実装は `cv2.adaptiveThreshold(_u8(v), 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, blockSize=2*int(a*6)+3, C=int(b*10))` —— a は局所窓のサイズ(blockSize)を 3〜15(奇数)に、b は局所平均から引く定数 C を 0〜10 に振る(C が大きいほど前景と判定される画素が減る)。
Border handling: replicate the edge pixel (nearest neighbour) (measured; tools/impl2/border_probe.py).
Blank frame (measured): feeding an image of uniform brightness makes every pixel foreground (1), regardless of the brightness. A frame that is blown out, occluded, or simply has no subject comes back as “100 % defective”, so test for uniformity upstream and reject such frames.
• 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.
cv_adaptive_mean 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
segmentation)threshold · otsu · canny · adaptive_gauss_thresh · sk_otsu · sk_li · sk_yen · sk_sauvola
*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.