AQC0907

Nanopublication — Computational Image Analysis - AQC0907

Claim 1: Computational Image Analysis - AQC0907

The artwork E Major [1] - Research on Harmony - Variations 10 (AQC0907) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-12-11. 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]: 1901x1901 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E3D922 18.5 yellow gold
2 7A91B6 16.0 blue-violet lightslategray
3 5F543F 13.0 yellow-orange dark brown
4 786B54 12.1 yellow-orange dimgray
5 67AFDE 9.6 blue cornflowerblue
6 9BAFCF 8.4 blue-violet lightsteelblue
7 5F789F 7.4 blue-violet grayish purple
8 F3DA88 6.9 yellow-orange khaki
9 E9DEBE 4.2 yellow-orange wheat
10 3B3424 3.8 yellow-orange darkslategray
11 191206 0.3 orange black [Accent]
12 181A07 0.3 yellow-green very dark gray [Accent]
13 A4CDC8 0.3 green lightsteelblue [Accent]
14 D2EAED 0.3 blue-green white [Accent]
15 1E1C2A 0.3 violet very dark gray [Accent]

Color Families:

Family %
yellow-orange 40.1
blue-violet 31.8
yellow 18.5
blue 9.6
orange 0.3
yellow-green 0.3
green 0.3
blue-green 0.3
violet 0.3

Accent Colors:

Hex Family Name Chroma
191206 orange black 6.3
181A07 yellow-green very dark gray 10.8
A4CDC8 green lightsteelblue 14.1
D2EAED blue-green white 8.1
1E1C2A violet very dark gray 9.8

Texture Analysis

Metric Value
Global Roughness 0.19
Mean Local Roughness 0.034
Roughness Uniformity 0.034
Edge Density 0.177
Mean Gradient Magnitude 0.264
Gradient Variance 0.113
Gradient Smoothness 0.0
Directional Coherence 0.013
Pattern Complexity 0.125
Pattern Repetition 1.0
Detail Frequency Ratio 0.673
Spatial Variation 0.111
Texture Consistency 0.662

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.585
Brightness Variance 0.19
Brightness Uniformity 0.675
Brightness Skewness -0.176
Brightness Entropy 7.442
Rms Contrast 0.19
Michelson Contrast 1.0
Weber Contrast 0.594
Mean Local Contrast 0.038
Contrast Uniformity 0.046
Dynamic Range 1.0
Effective Dynamic Range 0.58
Shadow Percentage 9.379
Midtone Percentage 55.137
Highlight Percentage 35.484
Shadow Clipping 0.005
Highlight Clipping 0.002
Tonal Balance 0.168
Fine Contrast 0.018
Medium Contrast 0.046
Coarse Contrast 0.056
Multiscale Contrast Ratio 0.326
Edge Contrast 0.264
Contrast Clustering 0.338

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.711
Color Clustering 0.501
Color Transition Smoothness 0.324
Transition Uniformity 0.243
Sharp Transition Ratio 0.1
Transition Directionality 0.021
Mean Saturation 0.449
Saturation Variance 0.049
Low Saturation Ratio 0.251
Medium Saturation Ratio 0.559
High Saturation Ratio 0.19
Saturation Clustering 0.998
Hue Concentration 0.18
Complementary Balance 0.227
Analogous Dominance 0.577
Temperature Bias 0.041

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 (2025). E Major - Research on Harmony - Variations 10 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0907.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/e-major-research-on-harmony-variations-10_icv.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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