AQC0501

Nanopublication — Computational Image Analysis - AQC0501

by Arnaud Quercy · Salut d'amour · 2023

Claim 1: Computational Image Analysis - AQC0501

Computational image analysis [3] of artwork Salut [1] d'amour (AQC0501) [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 2025-12-17.

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: 2025-12-17.

Color Analysis

Rank Color Hex % Family Name
1 BD8861 19.0 orange peru
2 B07F59 18.2 orange indianred
3 C9916A 15.8 orange darksalmon
4 D79E78 9.8 orange tan
5 270A04 9.4 red-orange very dark red
6 A27450 9.3 orange burnt sienna
7 7E0F15 7.4 red-orange maroon
8 401D16 6.6 red-orange very dark red
9 D5C2AF 3.9 orange silver
10 563730 0.7 red-orange dark brown

Color Families:

Family %
orange 75.9
red-orange 24.1

Texture Analysis

Metric Value
Global Roughness 0.206
Mean Local Roughness 0.028
Roughness Uniformity 0.023
Edge Density 0.108
Mean Gradient Magnitude 0.164
Gradient Variance 0.046
Gradient Smoothness 0.0
Directional Coherence 0.105
Pattern Complexity 0.171
Pattern Repetition 1.0
Detail Frequency Ratio 0.642
Spatial Variation 0.097
Texture Consistency 0.487

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.481
Brightness Variance 0.206
Brightness Uniformity 0.571
Brightness Skewness -0.935
Brightness Entropy 6.733
Rms Contrast 0.206
Michelson Contrast 1.0
Weber Contrast 0.799
Mean Local Contrast 0.022
Contrast Uniformity 0.126
Dynamic Range 0.984
Effective Dynamic Range 0.627
Shadow Percentage 24.044
Midtone Percentage 66.096
Highlight Percentage 9.86
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.019
Medium Contrast 0.028
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.164
Contrast Clustering 0.513

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.747
Color Clustering 0.687
Color Transition Smoothness 0.589
Transition Uniformity 0.699
Sharp Transition Ratio 0.1
Transition Directionality 0.108
Mean Saturation 0.548
Saturation Variance 0.03
Low Saturation Ratio 0.039
Medium Saturation Ratio 0.777
High Saturation Ratio 0.183
Saturation Clustering 0.999
Hue Concentration 0.988
Complementary Balance 0.0
Analogous Dominance 1.0
Temperature Bias 1.0

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). Salut d'amour — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0501.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2023/01/salut-damour_5n2.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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