AQC0436

Nanopublication — Computational Image Analysis - AQC0436

by Arnaud Quercy · Eb minor - Reflexions 2 · 2023

Claim 1: Computational Image Analysis - AQC0436

Analysis record [3]: Eb minor - Reflexions [1] 2 (AQC0436) [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]: 1536x2048 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 9DA0A4 17.6 gray steel gray
2 AFB1B3 16.6 gray steel gray
3 8A9195 16.5 gray lightslategray
4 788084 14.7 gray gray
5 C3C2C3 14.2 gray silver
6 5F6C76 7.0 blue dimgray
7 DDB087 4.0 orange burlywood
8 3F4D59 3.9 blue-violet grayish purple
9 2B2B30 3.3 gray very dark gray
10 A67350 2.3 orange peru
11 E3AC45 0.3 yellow-orange sandybrown [Accent]
12 83403C 0.3 red-orange burnt sienna [Accent]
13 868035 0.3 yellow olivedrab [Accent]

Color Families:

Family %
gray 82.9
blue 7.0
orange 6.2
blue-violet 3.9
yellow-orange 0.3
red-orange 0.3
yellow 0.3

Accent Colors:

Hex Family Name Chroma
E3AC45 yellow-orange sandybrown 59.8
83403C red-orange burnt sienna 32.2
868035 yellow olivedrab 40.8

Texture Analysis

Metric Value
Global Roughness 0.145
Mean Local Roughness 0.017
Roughness Uniformity 0.018
Edge Density 0.052
Mean Gradient Magnitude 0.146
Gradient Variance 0.041
Gradient Smoothness 0.0
Directional Coherence 0.005
Pattern Complexity 0.112
Pattern Repetition 1.0
Detail Frequency Ratio 0.615
Spatial Variation 0.078
Texture Consistency 0.661

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.588
Brightness Variance 0.145
Brightness Uniformity 0.753
Brightness Skewness -0.919
Brightness Entropy 7.085
Rms Contrast 0.145
Michelson Contrast 1.0
Weber Contrast 0.466
Mean Local Contrast 0.019
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.494
Shadow Percentage 6.469
Midtone Percentage 60.035
Highlight Percentage 33.496
Shadow Clipping 0.001
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.024
Coarse Contrast 0.038
Multiscale Contrast Ratio 0.219
Edge Contrast 0.146
Contrast Clustering 0.339

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.719
Color Clustering 0.618
Color Transition Smoothness 0.624
Transition Uniformity 0.718
Sharp Transition Ratio 0.1
Transition Directionality 0.002
Mean Saturation 0.123
Saturation Variance 0.02
Low Saturation Ratio 0.886
Medium Saturation Ratio 0.108
High Saturation Ratio 0.006
Saturation Clustering 1.0
Hue Concentration 0.217
Complementary Balance 0.179
Analogous Dominance 0.597
Temperature Bias -0.199

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). Eb minor - Reflexions 2 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0436.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2023/01/eb-minor-reflexions-2_4xs.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)

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