AQC0924

Nanopublication — Computational Image Analysis - AQC0924

by Arnaud Quercy · C Major - Research on Harmony - Variations 17 · 2025

Claim 1: Computational Image Analysis - AQC0924

Analysis record [3]: C Major [1] - Research on Harmony - Variations 17 (AQC0924) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2025-12-11.

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]: 1821x2731 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E8AD86 37.6 orange burlywood
2 ECB5C0 22.8 red lightpink
3 EA4E39 12.2 red-orange tomato
4 ECD7C8 8.7 orange bisque
5 524947 7.0 gray darkslategray
6 D03945 3.2 red-orange crimson
7 F2EBE4 2.8 white white
8 EC9119 2.0 orange goldenrod
9 441C1A 1.9 red-orange very dark red
10 E3B75C 1.8 yellow-orange sandybrown

Color Families:

Family %
orange 48.3
red 22.8
red-orange 17.3
gray 7.0
white 2.8
yellow-orange 1.8

Texture Analysis

Metric Value
Global Roughness 0.178
Mean Local Roughness 0.009
Roughness Uniformity 0.022
Edge Density 0.01
Mean Gradient Magnitude 0.069
Gradient Variance 0.061
Gradient Smoothness 0.0
Directional Coherence 0.188
Pattern Complexity 0.11
Pattern Repetition 1.0
Detail Frequency Ratio 0.601
Spatial Variation 0.107
Texture Consistency 0.625

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.677
Brightness Variance 0.178
Brightness Uniformity 0.737
Brightness Skewness -1.221
Brightness Entropy 6.59
Rms Contrast 0.178
Michelson Contrast 0.992
Weber Contrast 0.523
Mean Local Contrast 0.01
Contrast Uniformity 0.0
Dynamic Range 0.996
Effective Dynamic Range 0.592
Shadow Percentage 8.467
Midtone Percentage 18.21
Highlight Percentage 73.323
Shadow Clipping 0.0
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.004
Medium Contrast 0.013
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.069
Contrast Clustering 0.375

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.776
Color Clustering 0.29
Color Transition Smoothness 0.813
Transition Uniformity 0.588
Sharp Transition Ratio 0.1
Transition Directionality 0.212
Mean Saturation 0.393
Saturation Variance 0.049
Low Saturation Ratio 0.41
Medium Saturation Ratio 0.447
High Saturation Ratio 0.143
Saturation Clustering 0.999
Hue Concentration 0.961
Complementary Balance 0.0
Analogous Dominance 0.997
Temperature Bias 1.0

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). C Major - Research on Harmony - Variations 17 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0924.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/c-major-research-on-harmony-variations-17_iin.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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