texture op• Data kinds: image → image
• Call: fullseye.apply(img, "local_std", 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.*
*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):
▸ local_std: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ local_std: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ local_std: 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).*
Returns the standard deviation within a local window in the same units as the input (not normalized). Corresponds to HALCON's deviation_image (Calculate the standard deviation of gray values within rectangular windows.).
a sweeps the window width and b the window height, each over 3,5,7,9 (_k) — just like HALCON's deviation_image(image, width, height), a rectangular window can be used.
The only difference from std_filter is this, and it is what matters: std_filter normalises its output by that image's maximum, so values cannot be compared across images (in an image with only weak texture, the noise is lifted up to 1.0). This op does not divide, so its values can be compared as they are with other images and other times. HALCON's deviation_image does not normalise either, so in meaning this op is the faithful one. It is a different thing from the smoothing filter sigma_image (which smooths using σ).
Estimator and error (closed form): with the window's pixel count n = width x height, the mean-subtracted variance is made unbiased by n/(n-1), and the remaining bias of sigma is further removed by c4(n) ≈ 1 - 1/(4n). The estimate is therefore almost unbiased (measured: 99.8-100.1% of the ground truth). The relative standard error for 1 pixel is 1/sqrt(2(n-1)) — about 25% for a 3x3 window and about 7.9% for a 9x9 window (measurements agree with theory). When stating roughness or noise level as a number, give this width alongside.
If the input is in [0,1], the output is at least 0 and at most 0.55 (the maximum occurs when half the window is 0 and half is 1).
• gallery2d_texture_freq 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.
local_std 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_texture_freq — py -3.11 examples/gallery2d_texture_freq.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
texture)std_filter · local_bimodality · scale_select_std · bootstrap_std_error · structure_tensor_orientation · structure_tensor_coherence · gabor · sk_frangi
*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.