AQC0590

Nanopublication — Computational Image Analysis - AQC0590

Claim 1: Computational Image Analysis - AQC0590

Computational image analysis [3] of artwork Eb minor - Research [1] on Harmony (AQC0590) [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]: 2651x3535 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 484F5C 18.8 blue-violet grayish purple
2 3A1C50 13.8 violet very dark purple
3 9F9BA4 12.6 violet steel gray
4 878885 12.1 gray gray
5 6D6E6E 9.5 gray dimgray
6 8EDF9A 8.7 yellow-green lightgreen
7 C9C1B4 8.3 yellow-orange silver
8 E2ECD8 6.3 yellow-green gainsboro
9 6E386D 5.7 red-violet dusty mauve
10 231E1D 4.2 gray very dark gray
11 E3D598 0.3 yellow khaki [Accent]
12 BD7E99 0.3 red rosybrown [Accent]

Color Families:

Family %
violet 26.4
gray 25.7
blue-violet 18.8
yellow-green 15.0
yellow-orange 8.3
red-violet 5.7
yellow 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
E3D598 yellow khaki 32.2
BD7E99 red rosybrown 28.3

Texture Analysis

Metric Value
Global Roughness 0.237
Mean Local Roughness 0.041
Roughness Uniformity 0.045
Edge Density 0.139
Mean Gradient Magnitude 0.307
Gradient Variance 0.185
Gradient Smoothness 0.0
Directional Coherence 0.039
Pattern Complexity 0.123
Pattern Repetition 1.0
Detail Frequency Ratio 0.662
Spatial Variation 0.164
Texture Consistency 0.603

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.474
Brightness Variance 0.237
Brightness Uniformity 0.5
Brightness Skewness 0.255
Brightness Entropy 7.73
Rms Contrast 0.237
Michelson Contrast 1.0
Weber Contrast 0.788
Mean Local Contrast 0.043
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.741
Shadow Percentage 36.514
Midtone Percentage 38.499
Highlight Percentage 24.987
Shadow Clipping 0.019
Highlight Clipping 0.363
Tonal Balance 0.375
Fine Contrast 0.025
Medium Contrast 0.053
Coarse Contrast 0.078
Multiscale Contrast Ratio 0.315
Edge Contrast 0.307
Contrast Clustering 0.397

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.713
Color Clustering 0.869
Color Transition Smoothness 0.224
Transition Uniformity 0.0
Sharp Transition Ratio 0.1
Transition Directionality 0.045
Mean Saturation 0.27
Saturation Variance 0.048
Low Saturation Ratio 0.675
Medium Saturation Ratio 0.278
High Saturation Ratio 0.047
Saturation Clustering 0.999
Hue Concentration 0.401
Complementary Balance 0.15
Analogous Dominance 0.57
Temperature Bias -0.311

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). Eb minor - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0590.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/eb-minor-research-on-harmony_6lo.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)

5bf35e98508cbb97bb4ffb6c051679f58c3163cd9bffa998cf2cfcc36bc32ea6