AQC0803

Nanopublication — Computational Image Analysis - AQC0803

Terraces of Ronda - Ancient Path

Claim 1: Computational Image Analysis - AQC0803

The artwork Terraces [1] of Ronda - Ancient Path (AQC0803) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-02-04. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 2340x3510 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 B7B5A6 15.6 yellow steel gray
2 8BAFA8 12.9 green darkseagreen
3 C0C5BF 12.6 white silver
4 7DA199 11.9 green lightslategray
5 A3A291 11.8 yellow steel gray
6 87917B 10.3 yellow-green gray
7 2F2643 8.6 violet very dark purple
8 453568 6.8 violet dusty mauve
9 624F81 5.7 violet dusty mauve
10 657C71 3.8 yellow-green dimgray
11 544A3D 0.3 yellow-orange dark brown [Accent]
12 584B41 0.3 orange dark brown [Accent]
13 19141E 0.3 red-violet black [Accent]
14 3B6263 0.3 blue-green darkslategray [Accent]

Color Families:

Family %
yellow 27.4
green 24.8
violet 21.1
yellow-green 14.1
white 12.6
yellow-orange 0.3
orange 0.3
red-violet 0.3
blue-green 0.3

Accent Colors:

Hex Family Name Chroma
544A3D yellow-orange dark brown 9.2
584B41 orange dark brown 8.9
19141E red-violet black 8.5
3B6263 blue-green darkslategray 13.9

Texture Analysis

Metric Value
Global Roughness 0.182
Mean Local Roughness 0.015
Roughness Uniformity 0.014
Edge Density 0.034
Mean Gradient Magnitude 0.115
Gradient Variance 0.025
Gradient Smoothness 0.0
Directional Coherence 0.019
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.617
Spatial Variation 0.135
Texture Consistency 0.626

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.561
Brightness Variance 0.182
Brightness Uniformity 0.675
Brightness Skewness -0.989
Brightness Entropy 7.078
Rms Contrast 0.182
Michelson Contrast 1.0
Weber Contrast 0.691
Mean Local Contrast 0.015
Contrast Uniformity 0.091
Dynamic Range 0.953
Effective Dynamic Range 0.6
Shadow Percentage 17.054
Midtone Percentage 51.579
Highlight Percentage 31.367
Shadow Clipping 0.001
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.019
Coarse Contrast 0.029
Multiscale Contrast Ratio 0.303
Edge Contrast 0.115
Contrast Clustering 0.374

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.732
Color Clustering 0.87
Color Transition Smoothness 0.702
Transition Uniformity 0.825
Sharp Transition Ratio 0.1
Transition Directionality 0.024
Mean Saturation 0.211
Saturation Variance 0.024
Low Saturation Ratio 0.809
Medium Saturation Ratio 0.19
High Saturation Ratio 0.001
Saturation Clustering 1.0
Hue Concentration 0.474
Complementary Balance 0.054
Analogous Dominance 0.438
Temperature Bias -0.338

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). Terraces of Ronda - Ancient Path — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0803.html
https://arnaudquercy.art/fr/catalogue-raisonne/AQC0803.html

[2] Quercy, A. (2024). Terraces of Ronda - Ancient Path - Gallery. https://artquamanima.com/en/artworks/2024/01/terraces-of-ronda-ancient-path_8wi.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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