AQC0706

Nanopublication — Computational Image Analysis - AQC0706

by Arnaud Quercy · F# Minor - Research on Harmony - Variation 3 · 2024

Claim 1: Computational Image Analysis - AQC0706

Analysis record [3]: F# Minor [1] - Research on Harmony - Variation 3 (AQC0706) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 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]: 1446x2025 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 3E3D31 20.7 yellow darkslategray
2 466B58 15.5 yellow-green darkslategrey
3 AEB13E 14.3 yellow yellowgreen
4 66A09C 13.0 green cadetblue
5 D1B782 10.3 yellow-orange tan
6 868E3B 9.8 yellow olivedrab
7 ED9E7F 7.9 orange darksalmon
8 7C866E 4.5 yellow-green gray
9 EC9B2B 3.3 orange goldenrod
10 F3E4C5 0.9 yellow-orange bisque
11 4F7B85 0.3 blue-green blue gray [Accent]

Color Families:

Family %
yellow 44.7
yellow-green 19.9
green 13.0
yellow-orange 11.2
orange 11.1
blue-green 0.3

Accent Colors:

Hex Family Name Chroma
4F7B85 blue-green blue gray 15.6

Texture Analysis

Metric Value
Global Roughness 0.18
Mean Local Roughness 0.009
Roughness Uniformity 0.024
Edge Density 0.016
Mean Gradient Magnitude 0.062
Gradient Variance 0.049
Gradient Smoothness 0.0
Directional Coherence 0.288
Pattern Complexity 0.096
Pattern Repetition 1.0
Detail Frequency Ratio 0.64
Spatial Variation 0.109
Texture Consistency 0.67

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.503
Brightness Variance 0.18
Brightness Uniformity 0.642
Brightness Skewness -0.248
Brightness Entropy 7.062
Rms Contrast 0.18
Michelson Contrast 1.0
Weber Contrast 0.689
Mean Local Contrast 0.009
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.533
Shadow Percentage 21.297
Midtone Percentage 56.322
Highlight Percentage 22.381
Shadow Clipping 0.001
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.005
Medium Contrast 0.012
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.062
Contrast Clustering 0.33

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.76
Color Clustering 0.423
Color Transition Smoothness 0.825
Transition Uniformity 0.665
Sharp Transition Ratio 0.1
Transition Directionality 0.288
Mean Saturation 0.416
Saturation Variance 0.037
Low Saturation Ratio 0.244
Medium Saturation Ratio 0.712
High Saturation Ratio 0.044
Saturation Clustering 0.999
Hue Concentration 0.598
Complementary Balance 0.006
Analogous Dominance 0.676
Temperature Bias 0.058

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 (2024). F# Minor - Research on Harmony - Variation 3 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0706.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/f-minor-research-on-harmony-variation-3_7us.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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