morph_chan_vese — SEGMENTATION `contour` op

• Data kinds: image2d × mask → table

• Call: import fullseye as fs; fs.ledger.morph_chan_vese(image, init, *, n_iter: 'int' = 100, smoothing: 'int' = 1, lambda1: 'float' = 1.0, lambda2: 'float' = 1.0, phase: 'int' = 0, record_every: 'int' = 0) -> 'Dict[str, object]' (to call the implementation directly, import segcontour; segcontour.morph_chan_vese(image, init, *, n_iter: 'int' = 100, smoothing: 'int' = 1, lambda1: 'float' = 1.0, lambda2: 'float' = 1.0, phase: 'int' = 0, record_every: 'int' = 0) -> 'Dict[str, object]'; from the registry, opssegmentation.get("morph_chan_vese"))

Usage

Morphological Chan–Vese (MorphACWE of Márquez-Neila–Baumela–Álvarez 2014). One iteration = data step + smoothing step (morph_chan_vese).

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

データ段: c1 = 内側の平均、c0 = 外側の平均、aux = |∇u|(λ1 (I − c1)² − λ2 (I − c0)²)、aux < 0 の画素を内側、

aux > 0 を外側に(|∇u| ≠ 0 = 境界の近くだけが動く)。平滑段: 曲率の作用素(SI∘IS と IS∘SI を交互)を `smoothing` 回。

`phase` = 最初に使う作用素(0 = SI∘IS)。skimage の実装は交互の位相をモジュールの大域状態に持つので、

同じ入力でも直前の呼び出しで結果が変わりうる —— ここは呼び出しごとに `phase` から始める(決定的)。

当てはめのエネルギー E_fit = λ1 Σ_in (I − c1)² + λ2 Σ_out (I − c0)² を各段の前後で記録する。

門(厳密): c を固定した E_fit は画素ごとの和なので、データ段で反転した画素はどれも E_fit を下げ(または等しく)、

その後 c を平均に更新するとさらに下がる ⇒ データ段の前後で E_fit は単調非増加(`n_data_increase` = 0)。

平滑段は E_fit を上げうる(`fit_after_smooth` で実測)。

返り値: `mask、fit_before_data / fit_after_data / fit_after_smooth(反復ごと)、n_data_increase`、

`history、n_iter、n_changed`(反復ごとに変わった画素数)。

Detailed usage guide

• halcon_segmentation family guide

Background guides (the physics and conventions behind this op)

• active_contours_and_level_sets — 変分・動的輪郭とレベルセット — どの輪郭がどこで止まり、何を保証するか

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)

• poc_active_contours — py -3.11 examples/poc_active_contours.py

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

class_ndim_norm

Same category (`contour`)

snake_evolve · gvf_field · chan_vese_energy · chan_vese_evolve · morph_geodesic_ac · edge_stop_g · level_set_reinit · drle_evolve

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

morph_chan_vese — SEGMENTATION contour op