AQC0441

Nanopublication — Computational Image Analysis - AQC0441

Claim 1: Computational Image Analysis - AQC0441

K-means clustering analysis [3] (10 colors) performed on artwork B minor - Reflexions [1] 6 (AQC0441) [2] by Arnaud Quercy [2] on 2026-02-04. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

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]: 1536x2048 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 AC9379 15.3 orange rosybrown
2 DAAE84 13.8 orange burlywood
3 8C7B64 13.4 yellow-orange gray
4 80533B 12.0 orange burnt sienna
5 5D3A23 10.8 orange russet
6 5E5D52 9.0 yellow dimgray
7 8C4D1C 8.4 orange russet
8 AB5E35 6.8 orange burnt sienna
9 D89642 6.4 orange peru
10 2F1B0D 3.9 orange very dark orange
11 A62D1C 0.3 red-orange brown [Accent]
12 6C647C 0.3 violet dusty mauve [Accent]
13 343F42 0.3 blue-green darkslategray [Accent]

Color Families:

Family %
orange 77.6
yellow-orange 13.4
yellow 9.0
red-orange 0.3
violet 0.3
blue-green 0.3

Accent Colors:

Hex Family Name Chroma
A62D1C red-orange brown 61.8
6C647C violet dusty mauve 14.4
343F42 blue-green darkslategray 5.0

Texture Analysis

Metric Value
Global Roughness 0.164
Mean Local Roughness 0.008
Roughness Uniformity 0.009
Edge Density 0.015
Mean Gradient Magnitude 0.089
Gradient Variance 0.015
Gradient Smoothness 0.0
Directional Coherence 0.011
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.568
Spatial Variation 0.097
Texture Consistency 0.662

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.468
Brightness Variance 0.164
Brightness Uniformity 0.65
Brightness Skewness -0.052
Brightness Entropy 7.319
Rms Contrast 0.164
Michelson Contrast 1.0
Weber Contrast 0.618
Mean Local Contrast 0.01
Contrast Uniformity 0.006
Dynamic Range 0.882
Effective Dynamic Range 0.525
Shadow Percentage 20.968
Midtone Percentage 64.583
Highlight Percentage 14.449
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.114
Fine Contrast 0.004
Medium Contrast 0.012
Coarse Contrast 0.026
Multiscale Contrast Ratio 0.163
Edge Contrast 0.089
Contrast Clustering 0.338

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.683
Color Clustering 0.522
Color Transition Smoothness 0.756
Transition Uniformity 0.894
Sharp Transition Ratio 0.1
Transition Directionality 0.013
Mean Saturation 0.469
Saturation Variance 0.052
Low Saturation Ratio 0.268
Medium Saturation Ratio 0.532
High Saturation Ratio 0.2
Saturation Clustering 1.0
Hue Concentration 0.977
Complementary Balance 0.002
Analogous Dominance 0.996
Temperature Bias 0.995

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 (2022). B minor - Reflexions 6 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0441.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2022/01/b-minor-reflexions-6_4zq.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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