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On the Quantification of Visual Texture Complexity.

Fereshteh Mirjalili1, Jon Yngve Hardeberg1

  • 1Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway.

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Summary
This summary is machine-generated.

Human perception of texture complexity relies on visual cues like randomness and color. Luminance contrast in the image

Keywords:
Gabor filteringco-occurrence matrixcolor spacefirst-order image descriptorslocal binary patterntexture complexityvisual perception

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Area of Science:

  • Visual perception
  • Image analysis
  • Psychophysics

Background:

  • Texture complexity is a key attribute of visual perception.
  • Understanding human interpretation of texture complexity is limited.
  • Previous research has not fully explored the relationship between visual texture complexity and computational image features.

Purpose of the Study:

  • To visually quantify texture complexity and related attributes in textile fabrics.
  • To investigate the relationship between visually perceived texture complexity and computational texture measures.
  • To identify optimal color spaces and image channels for quantifying visual texture complexity.

Main Methods:

  • A psychophysical experiment was conducted to gather visual scores for texture attributes.
  • Principal Component Analysis (PCA) was used to identify underlying dimensions of visual texture perception.
  • Various texture measures (e.g., image statistics, local binary patterns, Gabor features) were computed in multiple color spaces.
  • Correlations between visual scores and computational measures were analyzed.

Main Results:

  • Visual texture complexity and homogeneity were found to be opposite ends of a single perceptual dimension.
  • Standard deviation of the luminance channel, Gabor filter energy, and co-occurrence matrix entropy showed strong correlations with visual texture complexity.
  • sRGB, YCbCr, and HSV color spaces outperformed I1I2I3 and CIELAB.
  • The luminance channel consistently showed the highest correlation with perceived texture complexity.

Conclusions:

  • Visual texture complexity is primarily driven by luminance variations (luminance contrast).
  • Chrominance channels do not adequately represent the spatial arrangements crucial for perceived texture complexity.
  • The luminance channel in specific color spaces (sRGB, YCbCr, HSV) is effective for quantifying visual texture complexity.