AQC0728

Nanopublication — Computational Image Analysis - AQC0728

Claim 1: Computational Image Analysis - AQC0728

Computational image analysis [3] of artwork Eb Major [1] - Research on Harmony - Variation 3 (AQC0728) [2] by Arnaud Quercy [2] using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 2026-02-04.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2983x3978 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 5A3D5D 18.7 red-violet dusty mauve
2 C9BCAC 16.1 yellow-orange silver
3 B96546 14.6 orange indianred
4 EDA729 11.3 yellow-orange goldenrod
5 A95430 10.3 orange burnt sienna
6 2E292C 7.1 gray very dark gray
7 CE7B5D 6.4 orange peru
8 BB96C7 6.2 red-violet steel gray
9 3B3B47 6.0 violet dusty mauve
10 6C5877 3.5 red-violet dusty mauve
11 541C0A 0.3 red-orange very dark red [Accent]
12 874C5B 0.3 red dimgray [Accent]

Color Families:

Family %
orange 31.2
red-violet 28.3
yellow-orange 27.3
gray 7.1
violet 6.0
red-orange 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
541C0A red-orange very dark red 34.7
874C5B red dimgray 27.1

Texture Analysis

Metric Value
Global Roughness 0.194
Mean Local Roughness 0.023
Roughness Uniformity 0.022
Edge Density 0.111
Mean Gradient Magnitude 0.172
Gradient Variance 0.05
Gradient Smoothness 0.0
Directional Coherence 0.013
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.651
Spatial Variation 0.144
Texture Consistency 0.465

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.481
Brightness Variance 0.194
Brightness Uniformity 0.596
Brightness Skewness 0.016
Brightness Entropy 7.323
Rms Contrast 0.194
Michelson Contrast 1.0
Weber Contrast 0.688
Mean Local Contrast 0.024
Contrast Uniformity 0.058
Dynamic Range 1.0
Effective Dynamic Range 0.576
Shadow Percentage 31.341
Midtone Percentage 42.607
Highlight Percentage 26.051
Shadow Clipping 0.001
Highlight Clipping 0.003
Tonal Balance 0.074
Fine Contrast 0.013
Medium Contrast 0.03
Coarse Contrast 0.04
Multiscale Contrast Ratio 0.319
Edge Contrast 0.172
Contrast Clustering 0.535

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.764
Color Clustering 0.473
Color Transition Smoothness 0.555
Transition Uniformity 0.667
Sharp Transition Ratio 0.1
Transition Directionality 0.017
Mean Saturation 0.435
Saturation Variance 0.063
Low Saturation Ratio 0.36
Medium Saturation Ratio 0.465
High Saturation Ratio 0.175
Saturation Clustering 0.999
Hue Concentration 0.678
Complementary Balance 0.027
Analogous Dominance 0.65
Temperature Bias 0.658

Methodology

This analysis employs standardized computational methods for objective image characterization. Color extraction uses k-means clustering algorithm. Texture analysis applies Haralick feature extraction. Brightness metrics include mean, variance, and distribution analysis. Spatial patterns are characterized through coherence and clustering measurements. All methods are deterministic and reproducible. Analysis performed by Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2024). Eb Major - Research on Harmony - Variation 3 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0728.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/eb-major-research-on-harmony-variation-3_83c.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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