AQC0910

Nanopublication — Computational Image Analysis - AQC0910

by Arnaud Quercy · F Major - Research on Harmony - Variations 11 · 2025

Claim 1: Computational Image Analysis - AQC0910

Analysis record [3]: F Major [1] - Research on Harmony - Variations 11 (AQC0910) [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]: 2016x2016 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 EC4F11 39.8 orange orangered
2 EE5724 21.6 orange chocolate
3 BE9B62 9.2 yellow-orange ochre
4 8E5E4A 7.5 orange burnt sienna
5 B07962 6.9 orange indianred
6 D3ACA6 5.0 red-orange tan
7 EAE6DA 3.6 yellow white
8 53423B 3.5 orange dark brown
9 B8383A 1.8 red-orange brown
10 3A150F 1.1 red-orange very dark red

Color Families:

Family %
orange 79.3
yellow-orange 9.2
red-orange 7.9
yellow 3.6

Texture Analysis

Metric Value
Global Roughness 0.123
Mean Local Roughness 0.015
Roughness Uniformity 0.022
Edge Density 0.06
Mean Gradient Magnitude 0.121
Gradient Variance 0.047
Gradient Smoothness 0.0
Directional Coherence 0.04
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.642
Spatial Variation 0.075
Texture Consistency 0.326

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.506
Brightness Variance 0.123
Brightness Uniformity 0.756
Brightness Skewness 0.972
Brightness Entropy 6.292
Rms Contrast 0.123
Michelson Contrast 1.0
Weber Contrast 0.363
Mean Local Contrast 0.017
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.396
Shadow Percentage 4.804
Midtone Percentage 85.53
Highlight Percentage 9.666
Shadow Clipping 0.0
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.021
Coarse Contrast 0.03
Multiscale Contrast Ratio 0.271
Edge Contrast 0.121
Contrast Clustering 0.674

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.784
Color Clustering 0.602
Color Transition Smoothness 0.699
Transition Uniformity 0.683
Sharp Transition Ratio 0.1
Transition Directionality 0.056
Mean Saturation 0.705
Saturation Variance 0.074
Low Saturation Ratio 0.116
Medium Saturation Ratio 0.25
High Saturation Ratio 0.634
Saturation Clustering 0.999
Hue Concentration 0.989
Complementary Balance 0.0
Analogous Dominance 1.0
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). F Major - Research on Harmony - Variations 11 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0910.html

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