AQC0887

Nanopublication — Computational Image Analysis - AQC0887

by Arnaud Quercy · A Minor - Research on Harmony - Variations 10 · 2025

Claim 1: Computational Image Analysis - AQC0887

Analysis record [3]: A Minor [1] - Research on Harmony - Variations 10 (AQC0887) [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]: 1967x2950 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E3B37C 26.7 orange burlywood
2 EE500E 19.5 orange orangered
3 EEC391 12.6 orange tan
4 DA9F6E 11.9 orange darksalmon
5 ECE1B5 10.4 yellow wheat
6 554B42 5.3 orange dark brown
7 ECE2DC 5.0 white white
8 E9DB37 3.5 yellow gold
9 D4374A 2.5 red-orange crimson
10 372217 2.5 orange very dark gray
11 E8ABB3 0.3 red lightpink [Accent]
12 857355 0.3 yellow-orange dimgray [Accent]

Color Families:

Family %
orange 78.5
yellow 13.9
white 5.0
red-orange 2.5
red 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
E8ABB3 red lightpink 23.5
857355 yellow-orange dimgray 19.1

Texture Analysis

Metric Value
Global Roughness 0.188
Mean Local Roughness 0.023
Roughness Uniformity 0.022
Edge Density 0.092
Mean Gradient Magnitude 0.186
Gradient Variance 0.064
Gradient Smoothness 0.0
Directional Coherence 0.004
Pattern Complexity 0.112
Pattern Repetition 1.0
Detail Frequency Ratio 0.623
Spatial Variation 0.111
Texture Consistency 0.635

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.664
Brightness Variance 0.188
Brightness Uniformity 0.717
Brightness Skewness -0.861
Brightness Entropy 7.125
Rms Contrast 0.188
Michelson Contrast 1.0
Weber Contrast 0.52
Mean Local Contrast 0.025
Contrast Uniformity 0.079
Dynamic Range 1.0
Effective Dynamic Range 0.608
Shadow Percentage 6.884
Midtone Percentage 26.458
Highlight Percentage 66.658
Shadow Clipping 0.0
Highlight Clipping 0.002
Tonal Balance 0.0
Fine Contrast 0.012
Medium Contrast 0.031
Coarse Contrast 0.046
Multiscale Contrast Ratio 0.264
Edge Contrast 0.186
Contrast Clustering 0.365

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.743
Color Clustering 0.336
Color Transition Smoothness 0.549
Transition Uniformity 0.573
Sharp Transition Ratio 0.1
Transition Directionality 0.005
Mean Saturation 0.51
Saturation Variance 0.071
Low Saturation Ratio 0.214
Medium Saturation Ratio 0.526
High Saturation Ratio 0.26
Saturation Clustering 0.999
Hue Concentration 0.97
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). A Minor - Research on Harmony - Variations 10 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0887.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/a-minor-research-on-harmony-variations-10_i5n.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/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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