AQC0912

Nanopublication — Computational Image Analysis - AQC0912

by Arnaud Quercy · G Minor - Research on Harmony - Variations 12 · 2025

Claim 1: Computational Image Analysis - AQC0912

Analysis record [3]: G Minor [1] - Research on Harmony - Variations 12 (AQC0912) [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]: 2075x2075 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E8AD76 23.8 orange burlywood
2 EDBC87 19.8 orange tan
3 EA9F5C 19.7 orange sandybrown
4 EE8D3F 10.9 orange coral
5 8A6190 7.4 red-violet dusty mauve
6 C6AED0 7.4 red-violet silver
7 ED740B 4.4 orange darkorange
8 5D4D43 4.1 orange dark brown
9 E6D4D4 1.4 red-orange gainsboro
10 3C1F1A 1.1 red-orange very dark red
11 B77481 0.3 red rosybrown [Accent]
12 88693A 0.3 yellow-orange burnt sienna [Accent]

Color Families:

Family %
orange 82.7
red-violet 14.8
red-orange 2.5
red 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
B77481 red rosybrown 28.3
88693A yellow-orange burnt sienna 31.6

Texture Analysis

Metric Value
Global Roughness 0.129
Mean Local Roughness 0.016
Roughness Uniformity 0.021
Edge Density 0.023
Mean Gradient Magnitude 0.126
Gradient Variance 0.046
Gradient Smoothness 0.0
Directional Coherence 0.023
Pattern Complexity 0.108
Pattern Repetition 1.0
Detail Frequency Ratio 0.629
Spatial Variation 0.062
Texture Consistency 0.521

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.667
Brightness Variance 0.129
Brightness Uniformity 0.806
Brightness Skewness -1.817
Brightness Entropy 6.516
Rms Contrast 0.129
Michelson Contrast 1.0
Weber Contrast 0.404
Mean Local Contrast 0.018
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.424
Shadow Percentage 4.17
Midtone Percentage 25.963
Highlight Percentage 69.867
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.022
Coarse Contrast 0.031
Multiscale Contrast Ratio 0.275
Edge Contrast 0.126
Contrast Clustering 0.479

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.776
Color Clustering 0.281
Color Transition Smoothness 0.683
Transition Uniformity 0.691
Sharp Transition Ratio 0.1
Transition Directionality 0.035
Mean Saturation 0.5
Saturation Variance 0.036
Low Saturation Ratio 0.134
Medium Saturation Ratio 0.727
High Saturation Ratio 0.139
Saturation Clustering 0.999
Hue Concentration 0.908
Complementary Balance 0.0
Analogous Dominance 0.919
Temperature Bias 0.93

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). G Minor - Research on Harmony - Variations 12 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0912.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/g-minor-research-on-harmony-variations-12_ieb.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)

216c30880165eff539b33edc03f20d8480ca7336f882b3089b4e18c9592534fd