cv_adaptive_mean — 2D `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)

Usage

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)。

Detailed usage guide

• gallery2d_segmentation 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.

Runnable examples (verified samples that actually call this op)

• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py

Ops the type connects to (they accept `region` as input)

identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small

Same category (`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).*

cv_adaptive_mean — 2D segmentation op