AQC0831

Nanopublication — Computational Image Analysis - AQC0831

by Arnaud Quercy · F Minor - Research on Harmony - Variation 17 · 2025

Claim 1: Computational Image Analysis - AQC0831

Analysis record [3]: F Minor [1] - Research on Harmony - Variation 17 (AQC0831) [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]: 2407x3209 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 DDD4AD 17.0 yellow palegoldenrod
2 CEC19F 16.7 yellow-orange tan
3 B6AB99 15.2 yellow-orange steel gray
4 DEE0E1 14.4 white gainsboro
5 A29786 12.0 yellow-orange rosybrown
6 CBD0D9 7.7 blue-violet lightgray
7 2F272D 6.4 red-violet very dark gray
8 877873 4.7 orange gray
9 5B4266 4.3 red-violet dusty mauve
10 CC774B 1.5 orange peru
11 D78B75 0.3 red-orange darksalmon [Accent]

Color Families:

Family %
yellow-orange 43.9
yellow 17.0
white 14.4
red-violet 10.8
blue-violet 7.7
orange 6.2
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
D78B75 red-orange darksalmon 35.4

Texture Analysis

Metric Value
Global Roughness 0.197
Mean Local Roughness 0.022
Roughness Uniformity 0.022
Edge Density 0.12
Mean Gradient Magnitude 0.171
Gradient Variance 0.051
Gradient Smoothness 0.0
Directional Coherence 0.024
Pattern Complexity 0.114
Pattern Repetition 1.0
Detail Frequency Ratio 0.631
Spatial Variation 0.122
Texture Consistency 0.561

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.687
Brightness Variance 0.197
Brightness Uniformity 0.713
Brightness Skewness -1.391
Brightness Entropy 7.18
Rms Contrast 0.197
Michelson Contrast 1.0
Weber Contrast 0.588
Mean Local Contrast 0.023
Contrast Uniformity 0.051
Dynamic Range 1.0
Effective Dynamic Range 0.69
Shadow Percentage 9.326
Midtone Percentage 24.602
Highlight Percentage 66.072
Shadow Clipping 0.005
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.012
Medium Contrast 0.028
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.171
Contrast Clustering 0.439

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.737
Color Clustering 0.733
Color Transition Smoothness 0.573
Transition Uniformity 0.661
Sharp Transition Ratio 0.1
Transition Directionality 0.028
Mean Saturation 0.184
Saturation Variance 0.016
Low Saturation Ratio 0.879
Medium Saturation Ratio 0.114
High Saturation Ratio 0.007
Saturation Clustering 0.999
Hue Concentration 0.711
Complementary Balance 0.002
Analogous Dominance 0.805
Temperature Bias 0.817

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 17 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0831.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/f-minor-research-on-harmony-variation-17_97e.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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