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Kylberg Texture
Classification
Industry
|...
License: Unknown

Overview

A number of textured surfaces, including fabrics and surfaces of stone, were imaged in the
local surroundings. Textured surfaces were also arranged using articles such as rice grains,
sesame seeds and lentils.

  • 28 texture classes.
  • 160 unique texture patches per class. (Alternative dataset with
    12 rotations per each original patch, 160*12=1920 texture patches per class).
  • Texture patch size: 576x576 pixels.
  • File format: Lossless compressed 8 bit PNG.
  • All patches are normalized with a mean value of 127 and a standard deviation of 40.
  • One directory per texture class.
  • Files are
    named as follows: blanket1-d-p011-r180.png, where blanket1 is the class name, d original
    image sample number (possible values are a, b, c, or d), p011 is patch number 11, r180
    patch rotated 180 degrees.

Data Collection

Each texture class was imaged under only one light setting from one direction on the same distance.
The images were acquired with a Canon EOS 550d DSLR camera with a Sigma 17-70 mm zoom lens.
Focus and exposure were manually set. The 5*,* 184 × 3*,* 456 pixel size images were acquired
as lossless compressed raw fifiles (CR2). The raw fifiles were corrected for lens distortion,
chromatic aberration and vignetting formed by the Sigma lens. The corrections was performed
according to the settings in the “Adobe (SIGMA 17-70mm F2.8-4 DC Macro OS HSM, Canon)” lens
profifile in Adobe Photoshop CS5 .The images were then converted to gray scale and saved as
lossless PNG fifiles. Lens correction and raw conversion was done in Adobe Photoshop CS5.

Citation

Please use the following citation when referencing the dataset:

@TECHREPORT{Kylberg2011c,
author = {Gustaf Kylberg},
title = {The Kylberg Texture Dataset v. 1.0},
institution = {Centre for Image Analysis, Swedish University of Agricultural Sciences
and Uppsala University, Uppsala, Sweden},
year = {2011},
type = {External report (Blue series)},
number = {35},
month = {September},
url = {http://www.cb.uu.se/~gustaf/texture/}
}
Data Summary
Type
Image,
Amount
1.92K
Size
15.38GB
Provided by
Gustaf Kylberg
PhD in Image Analysis (2008 - 2014). Link to PhD thesis.Current affiliation: Vironova AB, Stockholm, Sweden.
| Amount 1.92K | Size 15.38GB
Kylberg Texture
Classification
Industry
License: Unknown

Overview

A number of textured surfaces, including fabrics and surfaces of stone, were imaged in the
local surroundings. Textured surfaces were also arranged using articles such as rice grains,
sesame seeds and lentils.

  • 28 texture classes.
  • 160 unique texture patches per class. (Alternative dataset with
    12 rotations per each original patch, 160*12=1920 texture patches per class).
  • Texture patch size: 576x576 pixels.
  • File format: Lossless compressed 8 bit PNG.
  • All patches are normalized with a mean value of 127 and a standard deviation of 40.
  • One directory per texture class.
  • Files are
    named as follows: blanket1-d-p011-r180.png, where blanket1 is the class name, d original
    image sample number (possible values are a, b, c, or d), p011 is patch number 11, r180
    patch rotated 180 degrees.

Data Collection

Each texture class was imaged under only one light setting from one direction on the same distance.
The images were acquired with a Canon EOS 550d DSLR camera with a Sigma 17-70 mm zoom lens.
Focus and exposure were manually set. The 5*,* 184 × 3*,* 456 pixel size images were acquired
as lossless compressed raw fifiles (CR2). The raw fifiles were corrected for lens distortion,
chromatic aberration and vignetting formed by the Sigma lens. The corrections was performed
according to the settings in the “Adobe (SIGMA 17-70mm F2.8-4 DC Macro OS HSM, Canon)” lens
profifile in Adobe Photoshop CS5 .The images were then converted to gray scale and saved as
lossless PNG fifiles. Lens correction and raw conversion was done in Adobe Photoshop CS5.

Citation

Please use the following citation when referencing the dataset:

@TECHREPORT{Kylberg2011c,
author = {Gustaf Kylberg},
title = {The Kylberg Texture Dataset v. 1.0},
institution = {Centre for Image Analysis, Swedish University of Agricultural Sciences
and Uppsala University, Uppsala, Sweden},
year = {2011},
type = {External report (Blue series)},
number = {35},
month = {September},
url = {http://www.cb.uu.se/~gustaf/texture/}
}
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