xsk_blob_doh — 2D `features` op

• Data kinds: image → feature

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

Usage

Number of detected blobs (speckle-like structures) (detected using one of LoG/DoG/DoH).

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

skimage.feature の Laplacian of Gaussian(LoG)/ Difference of

Gaussian(DoG)/ Determinant of Hessian(DoH)のいずれか(このコードは

3 種を共通実装しており、どれを使うかは呼び出し元がどの op 名で

登録したか —— `xsk_blob_log / xsk_blob_dog / xsk_blob_doh`

—— で決まる)を用いてブロブを検出し、その個数をそのまま返す

(feature 出力)。

`a が探索する最大スケール max_sigma` を 5〜25 の範囲で振る

(大きいほど大きなブロブまで拾う)。`b` が検出しきい値

`threshold` を 0.02〜0.17 で振る(小さいほど弱いブロブまで拾い、

検出数が増えやすい)。3 手法は速度・精度が異なる(LoG が最も正確

だが遅く、DoH はエッジに強い一方、小さいブロブを苦手とする、等)。

Detailed usage guide

• gallery2d_features 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_features — py -3.11 examples/gallery2d_features.py

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

identity

Same category (`features`)

blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat · sk_blur_effect

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

xsk_blob_doh — 2D features op