edges op• Data kinds: image → image
• Call: fullseye.apply(img, "corner_response", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: points_harris (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
*The output is shown in a viridis-like pseudo-colour (dark purple = low, yellow = high) so that a field of quantities — distance, phase, orientation, depth — can be read.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ corner_response: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ corner_response: other inputs (docs site)
*No colour (H,W,3) input is shown: this op treats the colour channel as a third spatial axis (it crosses channels). Call it per channel.*
Returns the Harris corner detection response (corner-likeness) as an image. Equivalent to HALCON's `points_harris` (Detect points of interest using the Harris operator.).
> The detailed description below is the original text — the summary and the headings are translated.
`a が構造テンソルを平滑化するガウシアンの σ を 0.5〜2.5 に振る。b は未使用（Harris の経験定数 k=0.04 は固定）。Sobel 勾配 Gx, Gy から構造テンソル成分 Gx², Gy², Gx*Gy をガウシアンで平滑化し、det - k*trace² を _signed01 で [0,1] に写す（0.5 が応答ゼロ、大きいほどコーナーらしく、小さいほどエッジらしい）。座標リストではなく応答マップを返す点が HALCON の points_harris（座標を返す）と異なる——極大点抽出は別途 _local_max` 等と組み合わせる必要がある。
Value comparability (measured): the output is normalised by that image's own maximum (the output maximum is always 1.0, and scaling the input leaves the output unchanged). Values therefore cannot be compared across images — a feature of the same strength takes a different value depending on what the strongest feature in that image happens to be, and in an image holding only weak features the noise is lifted to 1.0. To compare across images, rescale by a common reference.
• gallery2d_edges 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.
corner_response 0.40 0.50
▸ Load this pipeline · Load & run
• gallery2d_edges — py -3.11 examples/gallery2d_edges.py
• poc_document_scan — py -3.11 examples/poc_document_scan.py
• poc_matrix_code_reading — py -3.11 examples/poc_matrix_code_reading.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
edges)sobel_mag · prewitt_mag · roberts_mag · dog · edge_transition_width · grad_dir · log · sk_scharr
*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.