AQC0636

Nanopublication — Computational Image Analysis - AQC0636

Claim 1: Computational Image Analysis - AQC0636

The artwork F minor - Research [1] on Harmony - Variation 7 (AQC0636) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-02-04. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 2187x3280 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 E93216 21.9 red-orange orangered
2 512C24 19.9 red-orange dark brown
3 23150B 11.9 orange very dark gray
4 4A6378 11.7 blue-violet grayish purple
5 35475D 9.0 blue-violet grayish purple
6 6D808A 7.2 blue blue gray
7 735E57 6.2 orange dimgray
8 9C3A23 5.5 red-orange brown
9 ECC596 4.3 yellow-orange burlywood
10 DE8363 2.4 orange darksalmon
11 FBEBB2 0.3 yellow moccasin [Accent]
12 7BA5AF 0.3 blue-green cadetblue [Accent]

Color Families:

Family %
red-orange 47.3
blue-violet 20.7
orange 20.5
blue 7.2
yellow-orange 4.3
yellow 0.3
blue-green 0.3

Accent Colors:

Hex Family Name Chroma
FBEBB2 yellow moccasin 30.1
7BA5AF blue-green cadetblue 15.6

Texture Analysis

Metric Value
Global Roughness 0.161
Mean Local Roughness 0.018
Roughness Uniformity 0.024
Edge Density 0.068
Mean Gradient Magnitude 0.162
Gradient Variance 0.065
Gradient Smoothness 0.0
Directional Coherence 0.02
Pattern Complexity 0.113
Pattern Repetition 1.0
Detail Frequency Ratio 0.623
Spatial Variation 0.092
Texture Consistency 0.55

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.336
Brightness Variance 0.161
Brightness Uniformity 0.522
Brightness Skewness 0.816
Brightness Entropy 7.086
Rms Contrast 0.161
Michelson Contrast 1.0
Weber Contrast 0.728
Mean Local Contrast 0.021
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.58
Shadow Percentage 46.057
Midtone Percentage 49.0
Highlight Percentage 4.943
Shadow Clipping 0.008
Highlight Clipping 0.004
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.027
Coarse Contrast 0.048
Multiscale Contrast Ratio 0.185
Edge Contrast 0.162
Contrast Clustering 0.45

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.783
Color Clustering 0.461
Color Transition Smoothness 0.563
Transition Uniformity 0.548
Sharp Transition Ratio 0.1
Transition Directionality 0.025
Mean Saturation 0.575
Saturation Variance 0.066
Low Saturation Ratio 0.149
Medium Saturation Ratio 0.526
High Saturation Ratio 0.325
Saturation Clustering 0.998
Hue Concentration 0.518
Complementary Balance 0.207
Analogous Dominance 0.756
Temperature Bias 0.513

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). F minor - Research on Harmony - Variation 7 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0636.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/f-minor-research-on-harmony-variation-7_73k.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/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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