AQC0510

Nanopublication — Computational Image Analysis - AQC0510

by Arnaud Quercy · Pause déjeuner sur un banc · 2024

Claim 1: Computational Image Analysis - AQC0510

Computational image analysis [3] of artwork Pause [1] déjeuner sur un banc (AQC0510) [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 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]: 843x1124 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank	Hex	%	Family	Name
1	431D17	15.6	red-orange	very dark red
2	9C2415	14.9	red-orange	brown
3	70241D	12.3	red-orange	russet
4	271B17	11.8	orange	very dark gray
5	CE1D15	10.6	red-orange	firebrick
6	F3A850	10.1	orange	sandybrown
7	EE7B03	9.7	orange	darkorange
8	E1D6C2	8.6	yellow-orange	lightgray
9	873944	4.8	red-orange	burnt sienna
10	AD6E77	1.6	red	rosybrown
11	FFF4C7	0.3	yellow	lemonchiffon [Accent]

Color Families:

Family	%
red-orange	58.1
orange	31.6
yellow-orange	8.6
red	1.6
yellow	0.3

Accent Colors:

Hex	Family	Name	Chroma
FFF4C7	yellow	lemonchiffon	23.2

Texture Analysis

Metric	Value
Global Roughness	0.235
Mean Local Roughness	0.021
Roughness Uniformity	0.034
Edge Density	0.062
Mean Gradient Magnitude	0.14
Gradient Variance	0.093
Gradient Smoothness	0.0
Directional Coherence	0.101
Pattern Complexity	0.119
Pattern Repetition	1.0
Detail Frequency Ratio	0.629
Spatial Variation	0.149
Texture Consistency	0.543

Brightness & Contrast Analysis

Metric	Value
Mean Brightness	0.365
Brightness Variance	0.235
Brightness Uniformity	0.356
Brightness Skewness	0.867
Brightness Entropy	7.185
Rms Contrast	0.235
Michelson Contrast	1.0
Weber Contrast	0.817
Mean Local Contrast	0.02
Contrast Uniformity	0.0
Dynamic Range	1.0
Effective Dynamic Range	0.733
Shadow Percentage	64.085
Midtone Percentage	17.826
Highlight Percentage	18.09
Shadow Clipping	0.001
Highlight Clipping	0.004
Tonal Balance	0.0
Fine Contrast	0.013
Medium Contrast	0.026
Coarse Contrast	0.039
Multiscale Contrast Ratio	0.332
Edge Contrast	0.14
Contrast Clustering	0.457

Spatial Distribution Analysis

Metric	Value
Spatial Coherence	0.72
Color Clustering	0.433
Color Transition Smoothness	0.601
Transition Uniformity	0.366
Sharp Transition Ratio	0.1
Transition Directionality	0.068
Mean Saturation	0.677
Saturation Variance	0.07
Low Saturation Ratio	0.135
Medium Saturation Ratio	0.319
High Saturation Ratio	0.546
Saturation Clustering	0.998
Hue Concentration	0.969
Complementary Balance	0.0
Analogous Dominance	0.997
Temperature Bias	0.999

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). Pause déjeuner sur un banc — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0510.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/pause-dejeuner-sur-un-banc_5qk.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 type	computational analysis
Voice	third person
Epistemic status	empirical measurement
Methodology	computational analysis
Certainty	high

Checksum (SHA-256)

b5943e760863f4a23179094459d81ead26a99666275990cdc048510891ec6f22