register_capture_range — MATCHREG `intensityreg` op

• Data kinds: image2d → table

• Call: import fullseye as fs; fs.ledger.register_capture_range(fixed, method='ecc', offsets_px=(1, 2, 4, 8, 12, 16, 24, 32), n_directions=8, success_px=0.5, rotation_deg=0.0, modality='same', noise_sigma=0.0, margin=8, seed=0, method_kwargs=None) (to call the implementation directly, import intensityreg; intensityreg.register_capture_range(fixed, method='ecc', offsets_px=(1, 2, 4, 8, 12, 16, 24, 32), n_directions=8, success_px=0.5, rotation_deg=0.0, modality='same', noise_sigma=0.0, margin=8, seed=0, method_kwargs=None); from the registry, opsmatchreg.get("register_capture_range"))

• GPU: none (CPU reference implementation only)

Usage

For each size of initial misalignment, returns the fraction of registrations started from the identity that return to the truth (the capture range). → table.

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

大きさ d(offsets_px)の並進を n_directions 方向(+ 任意の回転 rotation_deg)で合成し(synthesize_moving)、method を恒等の初期値から走らせ、内側の画素の EPE の平均が success_px 未満なら成功。method が例外で止まったら失敗として数える(理由を failures に数える)。capture_range_px は、それ以下のすべての大きさで成功率が 0.9 以上になる最大の d (最小の大きさで届かなければ 0)。

ECC のような局所の最適化は、ずれが模様の大きさを超えると別の極値に落ちる。位相相関("phase")は大域の探索なので並進には範囲が広いが、回転が入ると崩れる —— 同じ表で比べられる。

Args:

fixed: 基準の 2-D 画像。

method: ACCURACY_METHODS。

offsets_px: 試す並進の大きさ [px] の並び(正、昇順でなくてよい)。

n_directions: 方向の数。

success_px: 成功とみなす EPE の平均の上限 [px]。

rotation_deg: 並進に加える回転(中心まわり、度)。

modality / noise_sigma / seed / method_kwargs: register_accuracy_map と同じ。

margin: 縁から除く画素数(大きなずれで moving の外から来る画素も除く)。

Returns:

dict(kind="capture_range")。rows(大きさごとの offset_px / success_rate /

median_epe / n_trials / failures)/ capture_range_px / method / success_px /

rotation_deg / modality。

Raises:

ValueError: 画像が不正 / method・modality が違う / 引数が値域外。

Detailed usage guide

• matching_registration 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)

• (none yet)

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

features_match · ransac_homography · ransac_fundamental · registration_to_flow2d

Same category (`intensityreg`)

register_ecc · register_mi · register_ffd · registration_to_flow2d · register_accuracy_map

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

register_capture_range — MATCHREG intensityreg op