variation op• 資料種類:video → table
• 呼叫: import fullseye as fs; fs.ledger.varmodel_fit(stack, spread='moderated', far=0.001, align='check', max_misalign_px=0.5, spread_floor=0.0, mask=None)(要直接呼叫實作,import varmodel; varmodel.varmodel_fit(stack, spread='moderated', far=0.001, align='check', max_misalign_px=0.5, spread_floor=0.0, mask=None);從台帳取用則 opsspc.get("varmodel_fit"))
由良品影像堆疊學習逐像素的變動模型(中心、離散度、容許帶半寬 k)(table)。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
stack is (n, H, W) or a list of equal 2-D images, all good. spread chooses the estimator (SPREADS): std (mean / per-pixel sample std, needs n >= 3), pooled (mean / one std for all valid pixels — few samples, homoscedastic noise), moderated (mean / empirical-Bayes shrinkage of every pixel's variance toward a prior estimated from all pixels, Smyth 2004 — few samples, heteroscedastic noise; the default) or mad (median / 1.4826 MAD — robust to a few defective images hidden in the "good" set, needs n >= 5). far is the declared two-sided per-pixel false-alarm rate; k follows from it (see varmodel_band_k) and is never tuned on defects.
Alignment: align="check" measures every sample's translation (varmodel_alignment) and REFUSES the stack if any exceeds max_misalign_px; align="translate" registers the samples to the median by a Fourier shift (all-pass, so white noise keeps its variance), re-checks the residual against max_misalign_px and marks the wrapped border invalid. A stack without structure (alignment["observable"] False) is neither refused nor shifted; its band is then only an intensity threshold on the overall variation. Stochastic textures, whose every sample shows a different pattern, are therefore not a variation-model problem (on DAGM 2007 Class1 it scores at chance — see the PoC).
spread_floor (>= 0, image units) lower-bounds the spread (8-bit data has pixels whose good samples are all equal); any remaining zero spread is refused. mask (H, W) bool limits the inspected area. Returns center, spread, k, far, n, df, spread_kind, lower / upper (the band), valid (bool), n_floored, d0 / s0 (the prior's degrees of freedom and scale: 0 for std / mad; for pooled and a fully pooled moderated prior d0 = valid pixels x (n - 1)), k_source, mc_far_rel_se and alignment (the offset report, after registration if any).
Raises ValueError on an empty / malformed / non-finite stack, too few samples, an unknown spread / align, far outside its range, a misaligned stack, a mask of the wrong shape, or zero spread left after the floor.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py
• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table 作為輸入)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
variation)varmodel_alignment · varmodel_band_k · varmodel_calibrate · varmodel_score
*Provenance: varmodel.py — SPC 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.