sp_lowlands_center — 2D `subpix` op

• Data kinds: image → contour

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

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

Usage

Returns the centroid of minimum 'basins' (flat regions lower than their surroundings) (equivalent to HALCON `lowlands_center`).

> The detailed description below is the original text — the summary and the headings are translated.

`sp_plateaus と同じ量子化による平坦領域抽出を行った上で、外周の画素がすべて厳密に高い（領域極小である）ものだけを残し、さらに深さ（外周の最小値との差）が min_depth = 0.01 + 0.40*a 以上のものだけを採用する。b` は未使用。戻り値は CONTOUR、各盆地につき 1 点（重心座標、単位はピクセル）。入力が 200,000 画素を超える場合は安全側で空集合を返す。

Detailed usage guide

• gallery2d_geometry family guide

Background guides (the physics and conventions behind this op)

• measurement_uncertainty — 計測の不確かさと校正の知識 — 「測れている」を主張するために

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_geometry — py -3.11 examples/gallery2d_geometry.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 (`subpix`)

sp_local_max_sub_pix · sp_local_min_sub_pix · sp_saddle_points_sub_pix · sp_critical_points_sub_pix · sp_plateaus

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

sp_lowlands_center — 2D subpix op