AQC0872

Nanopublication — Computational Image Analysis - AQC0872

by Arnaud Quercy · D Minor - Research on Harmony - Variations 9 · 2025

Claim 1: Computational Image Analysis - AQC0872

K-means clustering analysis [3] (10 colors) performed on artwork D Minor [1] - Research on Harmony - Variations 9 (AQC0872) [2] by Arnaud Quercy [2] on 2025-12-11. 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]: 1932x2898 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 D29D39 19.3 yellow-orange peru
2 E3AE4D 18.7 yellow-orange sandybrown
3 E8B076 17.1 orange burlywood
4 EDC591 13.2 yellow-orange tan
5 E37A92 11.8 red palevioletred
6 A07974 7.4 red-orange gray
7 56423D 4.6 red-orange dark brown
8 F0E6D5 2.9 yellow-orange white
9 C15665 2.8 red-orange indianred
10 311812 2.1 red-orange very dark red

Color Families:

Family %
yellow-orange 54.1
orange 17.1
red-orange 17.0
red 11.8

Texture Analysis

Metric Value
Global Roughness 0.15
Mean Local Roughness 0.021
Roughness Uniformity 0.023
Edge Density 0.082
Mean Gradient Magnitude 0.194
Gradient Variance 0.068
Gradient Smoothness 0.0
Directional Coherence 0.003
Pattern Complexity 0.109
Pattern Repetition 1.0
Detail Frequency Ratio 0.615
Spatial Variation 0.076
Texture Consistency 0.696

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.652
Brightness Variance 0.15
Brightness Uniformity 0.77
Brightness Skewness -1.518
Brightness Entropy 6.98
Rms Contrast 0.15
Michelson Contrast 1.0
Weber Contrast 0.371
Mean Local Contrast 0.025
Contrast Uniformity 0.02
Dynamic Range 1.0
Effective Dynamic Range 0.541
Shadow Percentage 5.76
Midtone Percentage 38.551
Highlight Percentage 55.689
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.011
Medium Contrast 0.031
Coarse Contrast 0.053
Multiscale Contrast Ratio 0.199
Edge Contrast 0.194
Contrast Clustering 0.304

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.739
Color Clustering 0.55
Color Transition Smoothness 0.516
Transition Uniformity 0.542
Sharp Transition Ratio 0.1
Transition Directionality 0.005
Mean Saturation 0.52
Saturation Variance 0.032
Low Saturation Ratio 0.134
Medium Saturation Ratio 0.674
High Saturation Ratio 0.191
Saturation Clustering 0.999
Hue Concentration 0.945
Complementary Balance 0.0
Analogous Dominance 0.993
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). D Minor - Research on Harmony - Variations 9 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0872.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/d-minor-research-on-harmony-variations-9_i08.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)

2bc4fe2850c030c46bdf0d448961500cb43a8314c9e6c8ec7f9e37de4f9e913e