cv_good_features — 2D `features` op

• Data kinds: image → feature

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

Usage

High-quality corner detection via the Shi-Tomasi method (a single scalar feature, OpenCV implementation). Evaluates "corner-ness" using the same minimum-eigenvalue criterion as cv_min_eigen, and detects the top corners after thinning them out with non-maximum suppression and a minimum-distance filter - here, only the number of detected corners is returned (0 if none).

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

HALCON に直接対応するものは無い。実装は `cv2.goodFeaturesToTrack(v, maxCorners=int(10+40*a), qualityLevel=0.01+0.1*b, minDistance=5)` —— a は検出上限数を 10〜50 に、b は品質しきい値(最強コーナーに対する相対比。大きいほど厳しく絞り込まれ検出数が減る)を 0.01〜0.11 に振る。最小距離は 5 画素に固定。

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).*

cv_good_features — 2D features op