AQC0477

Nanopublication — Computational Image Analysis - AQC0477

by Arnaud Quercy · The myth of Kyrnos · 2023

Claim 1: Computational Image Analysis - AQC0477

Computational image analysis [3] of artwork The [1] myth of Kyrnos (AQC0477) [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 2026-02-04.

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]: 526x701 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 D0B592 18.8 yellow-orange tan
2 CDA378 18.2 orange ochre
3 C3835C 11.6 orange peru
4 DBC8AA 11.1 yellow-orange wheat
5 93765E 9.5 orange gray
6 B0947A 9.4 orange rosybrown
7 755C43 7.5 orange dark brown
8 563C24 4.8 orange dark brown
9 B15626 4.6 orange burnt sienna
10 2B1A08 4.6 orange very dark gray
11 F59D92 0.3 red-orange lightsalmon [Accent]

Color Families:

Family %
orange 70.2
yellow-orange 29.8
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
F59D92 red-orange lightsalmon 37.2

Texture Analysis

Metric Value
Global Roughness 0.178
Mean Local Roughness 0.035
Roughness Uniformity 0.026
Edge Density 0.166
Mean Gradient Magnitude 0.229
Gradient Variance 0.071
Gradient Smoothness 0.0
Directional Coherence 0.01
Pattern Complexity 0.121
Pattern Repetition 1.0
Detail Frequency Ratio 0.629
Spatial Variation 0.063
Texture Consistency 0.73

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.582
Brightness Variance 0.178
Brightness Uniformity 0.694
Brightness Skewness -0.99
Brightness Entropy 7.302
Rms Contrast 0.178
Michelson Contrast 1.0
Weber Contrast 0.577
Mean Local Contrast 0.032
Contrast Uniformity 0.294
Dynamic Range 0.949
Effective Dynamic Range 0.6
Shadow Percentage 10.376
Midtone Percentage 48.963
Highlight Percentage 40.662
Shadow Clipping 0.002
Highlight Clipping 0.0
Tonal Balance 0.021
Fine Contrast 0.024
Medium Contrast 0.041
Coarse Contrast 0.056
Multiscale Contrast Ratio 0.434
Edge Contrast 0.229
Contrast Clustering 0.27

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.672
Color Clustering 0.601
Color Transition Smoothness 0.406
Transition Uniformity 0.536
Sharp Transition Ratio 0.1
Transition Directionality 0.01
Mean Saturation 0.413
Saturation Variance 0.034
Low Saturation Ratio 0.267
Medium Saturation Ratio 0.639
High Saturation Ratio 0.094
Saturation Clustering 0.998
Hue Concentration 0.979
Complementary Balance 0.002
Analogous Dominance 0.998
Temperature Bias 0.996

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 (2023). The myth of Kyrnos — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0477.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2023/01/the-myth-of-kyrnos_5dq.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)

ff735e695e768dfd95d5bf7491d4443e3e2d36b96e6b79189fe8a073947b90e4