AQC0832

Nanopublication — Computational Image Analysis - AQC0832

Claim 1: Computational Image Analysis - AQC0832

Analysis record [3]: F Minor [1] - Research on Harmony - Variation 18 (AQC0832) [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]: 2426x3235 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 C9CAC7 28.4 white silver
2 B7BBB9 20.1 gray steel gray
3 DAD8D3 17.3 white lightgray
4 99A6AD 8.0 blue steel gray
5 845E83 6.0 red-violet dusty mauve
6 857363 5.5 orange dimgray
7 A78784 5.2 red-orange rosybrown
8 604F4D 5.1 red-orange dimgrey
9 2B2121 2.3 red-orange very dark gray
10 AE4B44 2.2 red-orange burnt sienna
11 833142 0.3 red brown [Accent]
12 708BA8 0.3 blue-violet grayish purple [Accent]

Color Families:

Family %
white 45.7
gray 20.1
red-orange 14.8
blue 8.0
red-violet 6.0
orange 5.5
red 0.3
blue-violet 0.3

Accent Colors:

Hex Family Name Chroma
833142 red brown 37.9
708BA8 blue-violet grayish purple 18.1

Texture Analysis

Metric Value
Global Roughness 0.177
Mean Local Roughness 0.016
Roughness Uniformity 0.016
Edge Density 0.071
Mean Gradient Magnitude 0.144
Gradient Variance 0.034
Gradient Smoothness 0.0
Directional Coherence 0.006
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.606
Spatial Variation 0.115
Texture Consistency 0.634

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.679
Brightness Variance 0.177
Brightness Uniformity 0.739
Brightness Skewness -1.215
Brightness Entropy 7.022
Rms Contrast 0.177
Michelson Contrast 1.0
Weber Contrast 0.521
Mean Local Contrast 0.018
Contrast Uniformity 0.109
Dynamic Range 1.0
Effective Dynamic Range 0.529
Shadow Percentage 5.036
Midtone Percentage 26.375
Highlight Percentage 68.59
Shadow Clipping 0.005
Highlight Clipping 0.003
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.023
Coarse Contrast 0.038
Multiscale Contrast Ratio 0.223
Edge Contrast 0.144
Contrast Clustering 0.366

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.708
Color Clustering 0.722
Color Transition Smoothness 0.637
Transition Uniformity 0.772
Sharp Transition Ratio 0.1
Transition Directionality 0.006
Mean Saturation 0.119
Saturation Variance 0.02
Low Saturation Ratio 0.891
Medium Saturation Ratio 0.101
High Saturation Ratio 0.008
Saturation Clustering 1.0
Hue Concentration 0.688
Complementary Balance 0.057
Analogous Dominance 0.663
Temperature Bias 0.76

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). F Minor - Research on Harmony - Variation 18 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0832.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/f-minor-research-on-harmony-variation-18_97s.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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