AQC0637

Nanopublication — Computational Image Analysis - AQC0637

by Arnaud Quercy · F# Major - Research on Harmony - Variation 1 · 2024

Claim 1: Computational Image Analysis - AQC0637

Analysis record [3]: F# Major [1] - Research on Harmony - Variation 1 (AQC0637) [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]: 2277x3415 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 2E664E 21.1 yellow-green darkslategray
2 573E3D 13.7 red-orange darkslategrey
3 53806F 12.8 green dimgray
4 7FC552 10.1 yellow-green yellowgreen
5 75C3B2 9.6 green mediumaquamarine
6 705A55 9.4 red-orange dimgrey
7 1A1910 7.9 yellow black
8 9DDE6B 6.1 yellow-green lightgreen
9 DBD9CA 5.1 yellow lightgray
10 A08A76 4.1 orange gray
11 FAF4E6 0.3 yellow-orange white [Accent]

Color Families:

Family %
yellow-green 37.4
red-orange 23.1
green 22.5
yellow 13.0
orange 4.1
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
FAF4E6 yellow-orange white 7.0

Texture Analysis

Metric Value
Global Roughness 0.208
Mean Local Roughness 0.041
Roughness Uniformity 0.041
Edge Density 0.178
Mean Gradient Magnitude 0.318
Gradient Variance 0.178
Gradient Smoothness 0.0
Directional Coherence 0.016
Pattern Complexity 0.121
Pattern Repetition 1.0
Detail Frequency Ratio 0.644
Spatial Variation 0.122
Texture Consistency 0.614

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.448
Brightness Variance 0.208
Brightness Uniformity 0.535
Brightness Skewness 0.289
Brightness Entropy 7.526
Rms Contrast 0.208
Michelson Contrast 1.0
Weber Contrast 0.686
Mean Local Contrast 0.043
Contrast Uniformity 0.081
Dynamic Range 1.0
Effective Dynamic Range 0.69
Shadow Percentage 33.662
Midtone Percentage 46.368
Highlight Percentage 19.971
Shadow Clipping 0.129
Highlight Clipping 0.054
Tonal Balance 0.144
Fine Contrast 0.023
Medium Contrast 0.056
Coarse Contrast 0.086
Multiscale Contrast Ratio 0.268
Edge Contrast 0.318
Contrast Clustering 0.386

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.762
Color Clustering 0.574
Color Transition Smoothness 0.175
Transition Uniformity 0.0
Sharp Transition Ratio 0.1
Transition Directionality 0.018
Mean Saturation 0.418
Saturation Variance 0.031
Low Saturation Ratio 0.271
Medium Saturation Ratio 0.696
High Saturation Ratio 0.033
Saturation Clustering 0.996
Hue Concentration 0.437
Complementary Balance 0.127
Analogous Dominance 0.509
Temperature Bias -0.181

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# Major - Research on Harmony - Variation 1 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0637.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/f-major-research-on-harmony-variation-1_73y.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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