AQC0880

Nanopublication — Computational Image Analysis - AQC0880

Claim 1: Computational Image Analysis - AQC0880

Computational image analysis [3] of artwork F Major [1] - Research on Harmony - Variations 10 (AQC0880) [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 2025-12-11.

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]: 2031x3047 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 D79A3C 20.7 yellow-orange peru
2 E9AAB0 18.7 red lightpink
3 E2AD5C 14.1 yellow-orange sandybrown
4 5F463E 9.8 red-orange dark brown
5 E96377 7.0 red-orange lightcoral
6 EED8D0 6.9 orange gainsboro
7 E32C26 6.8 red-orange crimson
8 BC5E57 6.2 red-orange indianred
9 CD838D 5.0 red rosybrown
10 331914 4.7 red-orange very dark red
11 FDFAEB 0.3 yellow white [Accent]

Color Families:

Family %
yellow-orange 34.8
red-orange 34.6
red 23.7
orange 6.9
yellow 0.3

Accent Colors:

Hex Family Name Chroma
FDFAEB yellow white 8.2

Texture Analysis

Metric Value
Global Roughness 0.19
Mean Local Roughness 0.021
Roughness Uniformity 0.021
Edge Density 0.085
Mean Gradient Magnitude 0.183
Gradient Variance 0.058
Gradient Smoothness 0.0
Directional Coherence 0.007
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.616
Spatial Variation 0.112
Texture Consistency 0.816

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.593
Brightness Variance 0.19
Brightness Uniformity 0.68
Brightness Skewness -0.833
Brightness Entropy 7.399
Rms Contrast 0.19
Michelson Contrast 1.0
Weber Contrast 0.615
Mean Local Contrast 0.024
Contrast Uniformity 0.084
Dynamic Range 1.0
Effective Dynamic Range 0.62
Shadow Percentage 12.056
Midtone Percentage 45.965
Highlight Percentage 41.979
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.013
Fine Contrast 0.011
Medium Contrast 0.03
Coarse Contrast 0.049
Multiscale Contrast Ratio 0.231
Edge Contrast 0.183
Contrast Clustering 0.184

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.729
Color Clustering 0.484
Color Transition Smoothness 0.537
Transition Uniformity 0.597
Sharp Transition Ratio 0.1
Transition Directionality 0.004
Mean Saturation 0.506
Saturation Variance 0.051
Low Saturation Ratio 0.305
Medium Saturation Ratio 0.465
High Saturation Ratio 0.23
Saturation Clustering 0.999
Hue Concentration 0.946
Complementary Balance 0.0
Analogous Dominance 1.0
Temperature Bias 1.0

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 Major - Research on Harmony - Variations 10 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0880.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/f-major-research-on-harmony-variations-10_i34.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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