AQC0790

Nanopublication — Computational Image Analysis - AQC0790

by Arnaud Quercy · Fsharp Octaves - Reflexions 24 · 2024

Claim 1: Computational Image Analysis - AQC0790

Analysis record [3]: Fsharp [1] Octaves - Reflexions 24 (AQC0790) [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]: 2230x3345 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 2A3034 18.8 gray darkslategray
2 D1CDC0 15.8 yellow lightgray
3 C2BDAF 14.3 yellow silver
4 384644 11.0 green darkslategrey
5 929FA1 10.8 blue-green steel gray
6 236C56 9.7 green seagreen
7 338B62 6.6 yellow-green mediumseagreen
8 1B1E24 6.6 blue-violet very dark gray
9 73C599 5.2 yellow-green mediumaquamarine
10 676F62 1.2 yellow-green dimgray
11 958653 0.3 yellow-orange gray [Accent]

Color Families:

Family %
yellow 30.1
green 20.7
gray 18.8
yellow-green 13.1
blue-green 10.8
blue-violet 6.6
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
958653 yellow-orange gray 29.1

Texture Analysis

Metric Value
Global Roughness 0.252
Mean Local Roughness 0.009
Roughness Uniformity 0.012
Edge Density 0.021
Mean Gradient Magnitude 0.098
Gradient Variance 0.031
Gradient Smoothness 0.0
Directional Coherence 0.034
Pattern Complexity 0.111
Pattern Repetition 1.0
Detail Frequency Ratio 0.567
Spatial Variation 0.195
Texture Consistency 0.371

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.47
Brightness Variance 0.252
Brightness Uniformity 0.464
Brightness Skewness 0.059
Brightness Entropy 7.31
Rms Contrast 0.252
Michelson Contrast 1.0
Weber Contrast 0.792
Mean Local Contrast 0.012
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.671
Shadow Percentage 41.119
Midtone Percentage 26.025
Highlight Percentage 32.856
Shadow Clipping 0.021
Highlight Clipping 0.001
Tonal Balance 0.024
Fine Contrast 0.004
Medium Contrast 0.014
Coarse Contrast 0.03
Multiscale Contrast Ratio 0.148
Edge Contrast 0.098
Contrast Clustering 0.629

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.77
Color Clustering 0.87
Color Transition Smoothness 0.735
Transition Uniformity 0.779
Sharp Transition Ratio 0.1
Transition Directionality 0.048
Mean Saturation 0.254
Saturation Variance 0.045
Low Saturation Ratio 0.728
Medium Saturation Ratio 0.214
High Saturation Ratio 0.058
Saturation Clustering 0.999
Hue Concentration 0.852
Complementary Balance 0.011
Analogous Dominance 0.821
Temperature Bias -0.945

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). Fsharp Octaves - Reflexions 24 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0790.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/fsharp-octaves-reflexions-24_8rg.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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