sp_local_min_sub_pix — 2D `subpix` op

• Data kinds: image → contour

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

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

Usage

Detects local minima of the gray-value surface with sub-pixel accuracy (equivalent to HALCON `local_min_sub_pix`).

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

アルゴリズムは `sp_local_max_sub_pix と対称（3x3 の 2 次曲面フィットで勾配 0 点へサブピクセル補正）。a は極小の顕著さ（周囲の最大値からの深さ）のしきい値を 0.01〜0.31 に振り（thr = 0.01 + 0.30*a）、b` は未使用。戻り値は CONTOUR、座標の単位は入力画像のピクセル。暗いスポット・くぼみの検出に向く。

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_saddle_points_sub_pix · sp_critical_points_sub_pix · sp_plateaus · sp_lowlands_center

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

sp_local_min_sub_pix — 2D subpix op