segment op• Data kinds: image → region
• Call: fullseye.apply(img, "sg_normalized_cut_2", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*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.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ sg_normalized_cut_2: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ sg_normalized_cut_2: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ sg_normalized_cut_2: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Splits the image into two regions (bright / dark) using normalized cut on the intensity graph (spectral 2-way partitioning, Shi & Malik; no corresponding HALCON operator).
To keep the computational cost down, the image is subsampled to about sdim = round(10+b*14); then an affinity graph is built with weights from luminance difference and distance, and the 2nd eigenvector (Fiedler vector) of the generalised eigenvalue problem (D-W)y = lambda*D*y is thresholded at its median to split into 2 groups. a (0 to 1) sweeps the bandwidth in the luminance direction sig_i = 0.05 + a*0.5, and b (0 to 1) sweeps the subsampling resolution. The brighter group is upscaled to the original resolution by nearest neighbour and returned as a region.
• gallery2d_segmentation 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.
sg_normalized_cut_2 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
segment)sg_slic_superpixels · sg_felzenszwalb · sg_gmm_segment · sg_kmeans_intensity · sg_region_growing_seeded · sg_watershed_gradient
*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.