seg_rand — EMPROOF `score` op

• 数据种类:labels2d × labels2d → table

• 调用: import fullseye as fs; fs.ledger.seg_rand(a, b, ignore_label=None)(要直接调用实现,import segcompare; segcompare.seg_rand(a, b, ignore_label=None);从台账取用则 opsemproof.get("seg_rand"))

用法

> 该算子的说明尚无译文,以下照原文给出。

Rand index, adjusted Rand index and the adapted Rand error (CREMI / SNEMI).

With n_ij the contingency table, a_i / b_j its row and column sums and N the pixel count:

• rand_index = fraction of pixel pairs on which the two agree (same/different);

• adjusted_rand_index = Hubert & Arabie's chance-corrected version (1 = identical, about 0 = independent);

• adapted_rand_error = 1 - F over pairs of distinct pixels: pair precision = (sum n_ij^2 - N) / (sum b_j^2 - N) — of the pairs the candidate puts together, the fraction the truth also puts together — and recall = (sum n_ij^2 - N) / (sum a_i^2 - N) (Arganda-Carreras et al. 2015). The - N removes each pixel's pair with itself. scikit-image's adapted_rand_error returns the same error, but its precision is divided by the truth pairs (its code sums the rows of the truth-by-test table), i.e. it is this recall; its docstring says otherwise.

Raises ValueError: as seg_contingency; fewer than 2 counted pixels.

详细使用指南

• emproof 族使用指南

参考(示例数据、文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者、年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_em_split_merge_score — py -3.11 examples/poc_em_split_merge_score.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

seg_synapse_partners · seg_wiring_variation · seg_synapse_nri · seg_wiring_exposure

同类别(`score`)

seg_contingency · seg_variation_of_information

*Provenance: segcompare.py — EMPROOF 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

seg_rand — EMPROOF score op

用法