AQC0896 | NAN-COL000068

Nanopublication — Computational Image Analysis - AQC0896

G Major - Research on Harmony - Variations 11

Claim 1: Computational Image Analysis - AQC0896

K-means clustering (10 colors) performed on artwork G Major - Research on Harmony - Variations 11 (AQC0896) [1] by Arnaud Quercy [2] on 2025-12-11, according to IDS-CMP-2025 [3]. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

Context

Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 1838x2757 pixels. Analysis date: 2025-12-11.

A) Color Analysis

Rank Color Hex % Family Name
1 E69C5C 22.1 orange sandybrown
2 EBA969 19.7 orange darksalmon
3 ED7F31 19.6 orange peru
4 BB9F28 14.0 yellow-orange goldenrod
5 CBA935 12.1 yellow-orange ochre
6 EB7009 6.8 orange chocolate
7 C4BC5D 2.2 yellow ochre
8 64462C 1.2 orange dark brown
9 3B200D 1.2 orange very dark orange
10 DFD8C9 1.1 yellow-orange lightgray
11 842A11 0.3 red-orange russet [Accent]
12 F3F3E3 0.3 yellow-green white [Accent]

Color Families:

Family %
orange 70.6
yellow-orange 27.2
yellow 2.2
red-orange 0.3
yellow-green 0.3

Accent Colors:

Hex Family Name Chroma
842A11 red-orange russet 51.6
F3F3E3 yellow-green white 8.5

B) Texture Analysis

Metric Value
Global Roughness 0.09
Mean Local Roughness 0.013
Roughness Uniformity 0.017
Edge Density 0.024
Mean Gradient Magnitude 0.105
Gradient Variance 0.035
Gradient Smoothness 0.0
Directional Coherence 0.038
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.62
Spatial Variation 0.047
Texture Consistency 0.537

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.637
Brightness Variance 0.09
Brightness Uniformity 0.858
Brightness Skewness -2.5
Brightness Entropy 6.066
Rms Contrast 0.09
Michelson Contrast 1.0
Weber Contrast 0.224
Mean Local Contrast 0.014
Contrast Uniformity 0.0
Dynamic Range 0.996
Effective Dynamic Range 0.196
Shadow Percentage 2.145
Midtone Percentage 54.646
Highlight Percentage 43.209
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.007
Medium Contrast 0.018
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.105
Contrast Clustering 0.463

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.775
Color Clustering 0.21
Color Transition Smoothness 0.734
Transition Uniformity 0.761
Sharp Transition Ratio 0.1
Transition Directionality 0.055
Mean Saturation 0.689
Saturation Variance 0.021
Low Saturation Ratio 0.012
Medium Saturation Ratio 0.463
High Saturation Ratio 0.524
Saturation Clustering 0.999
Hue Concentration 0.984
Complementary Balance 0.0
Analogous Dominance 1.0
Temperature Bias 0.99

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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2025). G Major - Research on Harmony - Variations 11 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0896.html
  2. [2] Quercy, A. (2025). ORCID https://orcid.org/0009-0000-2662-7790
  3. [3] Quercy, A. (2026). Computational Image Analysis Standard. https://ideamorphism.org/en/measurements/2025/09/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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