AQC0580

Nanopublication — Computational Image Analysis - AQC0580

by Arnaud Quercy · In the Woods of Saint Germain · 2024

Claim 1: Computational Image Analysis - AQC0580

Analysis record [3]: In the Woods [1] of Saint Germain (AQC0580) [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]: 2118x3177 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 302E27 25.4 yellow very dark gray
2 423F3F 16.0 gray darkslategray
3 AFAC92 15.1 yellow steel gray
4 757467 11.3 yellow dimgray
5 5A5556 11.0 gray dimgrey
6 919081 9.3 yellow gray
7 B08F61 3.8 yellow-orange ochre
8 C9C8BA 3.0 yellow silver
9 191613 2.9 black black
10 BA7B34 2.1 orange peru
11 F7F3FC 0.3 violet white [Accent]

Color Families:

Family %
yellow 64.1
gray 27.0
yellow-orange 3.8
black 2.9
orange 2.1
violet 0.3

Accent Colors:

Hex Family Name Chroma
F7F3FC violet white 5.0

Texture Analysis

Metric Value
Global Roughness 0.2
Mean Local Roughness 0.031
Roughness Uniformity 0.03
Edge Density 0.141
Mean Gradient Magnitude 0.261
Gradient Variance 0.101
Gradient Smoothness 0.0
Directional Coherence 0.044
Pattern Complexity 0.13
Pattern Repetition 1.0
Detail Frequency Ratio 0.654
Spatial Variation 0.14
Texture Consistency 0.585

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.388
Brightness Variance 0.2
Brightness Uniformity 0.486
Brightness Skewness 0.377
Brightness Entropy 7.34
Rms Contrast 0.2
Michelson Contrast 1.0
Weber Contrast 0.75
Mean Local Contrast 0.034
Contrast Uniformity 0.075
Dynamic Range 1.0
Effective Dynamic Range 0.557
Shadow Percentage 48.79
Midtone Percentage 39.704
Highlight Percentage 11.506
Shadow Clipping 0.027
Highlight Clipping 0.005
Tonal Balance 0.002
Fine Contrast 0.016
Medium Contrast 0.042
Coarse Contrast 0.068
Multiscale Contrast Ratio 0.238
Edge Contrast 0.261
Contrast Clustering 0.415

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.705
Color Clustering 0.849
Color Transition Smoothness 0.303
Transition Uniformity 0.323
Sharp Transition Ratio 0.1
Transition Directionality 0.045
Mean Saturation 0.179
Saturation Variance 0.02
Low Saturation Ratio 0.881
Medium Saturation Ratio 0.103
High Saturation Ratio 0.016
Saturation Clustering 0.999
Hue Concentration 0.762
Complementary Balance 0.009
Analogous Dominance 0.86
Temperature Bias 0.744

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). In the Woods of Saint Germain — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0580.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/in-the-woods-of-saint-germain_6hs.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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