AQC0863

Nanopublication — Computational Image Analysis - AQC0863

Claim 1: Computational Image Analysis - AQC0863

The artwork F# Minor [1] - Research on Harmony - Variation 6 (AQC0863) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-12-09. 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]: 2403x3204 pixels. Analysis date: 2025-12-09.

Color Analysis

Rank Color Hex % Family Name
1 DED9C7 25.8 yellow lightgray
2 C2C9C1 16.8 yellow-green silver
3 ABB9B3 14.6 green steel gray
4 528491 9.3 blue-green blue gray
5 9EAB95 9.0 yellow-green steel gray
6 C6BDA2 8.6 yellow-orange tan
7 889373 5.9 yellow-green gray
8 B49B72 3.5 yellow-orange ochre
9 687358 3.4 yellow-green dimgray
10 353732 3.1 gray darkslategray
11 F0B39E 0.3 orange burlywood [Accent]
12 EEA89F 0.3 red-orange burlywood [Accent]

Color Families:

Family %
yellow-green 35.1
yellow 25.8
green 14.6
yellow-orange 12.0
blue-green 9.3
gray 3.1
orange 0.3
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
F0B39E orange burlywood 26.9
EEA89F red-orange burlywood 29.2

Texture Analysis

Metric Value
Global Roughness 0.158
Mean Local Roughness 0.017
Roughness Uniformity 0.015
Edge Density 0.091
Mean Gradient Magnitude 0.155
Gradient Variance 0.029
Gradient Smoothness 0.0
Directional Coherence 0.007
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.607
Spatial Variation 0.061
Texture Consistency 0.645

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.695
Brightness Variance 0.158
Brightness Uniformity 0.772
Brightness Skewness -1.186
Brightness Entropy 7.044
Rms Contrast 0.158
Michelson Contrast 1.0
Weber Contrast 0.461
Mean Local Contrast 0.019
Contrast Uniformity 0.222
Dynamic Range 1.0
Effective Dynamic Range 0.467
Shadow Percentage 3.3
Midtone Percentage 28.325
Highlight Percentage 68.376
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.024
Coarse Contrast 0.039
Multiscale Contrast Ratio 0.232
Edge Contrast 0.155
Contrast Clustering 0.355

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.735
Color Clustering 0.801
Color Transition Smoothness 0.617
Transition Uniformity 0.812
Sharp Transition Ratio 0.1
Transition Directionality 0.009
Mean Saturation 0.16
Saturation Variance 0.015
Low Saturation Ratio 0.871
Medium Saturation Ratio 0.129
High Saturation Ratio 0.0
Saturation Clustering 1.0
Hue Concentration 0.44
Complementary Balance 0.056
Analogous Dominance 0.625
Temperature Bias -0.031

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 (2025). F# Minor - Research on Harmony - Variation 6 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0863.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/f-minor-research-on-harmony-variation-6_9ju.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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