features op• Data kinds: image → feature
• Call: fullseye.apply(img, "xsk_blob_log", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
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)。
• gallery2d_features family guide
• 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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
xsk_blob_log 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_features — py -3.11 examples/gallery2d_features.py
feature as input)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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.