AQC1137

Nanopublication — Computational Image Analysis - AQC1137

Ab7#11 - Research on Harmony - Variations 3

Claim 1: Computational Image Analysis - AQC1137

K-means clustering (10 colors) performed on artwork Ab7#11 - Research on Harmony - Variations 3 (AQC1137) [1] by Arnaud Quercy [2] on 2026-10-02, according to IDS-CMP-2025 [3]. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

Context

Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 1952x2794 pixels. Analysis date: 2026-10-02.

A) Color Analysis

Rank Color Hex % Family Name
1 D2C8B7 26.2 yellow-orange silver
2 B4A792 16.9 yellow-orange rosybrown
3 C3B8A6 14.5 yellow-orange steel gray
4 2B2B33 9.8 violet very dark gray
5 0A0607 7.3 black black
6 716363 7.0 red-orange dimgray
7 8A472A 5.4 orange russet
8 A75B49 5.1 red-orange burnt sienna
9 5C5545 5.0 yellow-orange dark brown
10 987D67 3.0 orange gray
11 562C33 0.3 red darkslategray [Accent]

Color Families:

Family %
yellow-orange 62.5
red-orange 12.1
violet 9.8
orange 8.4
black 7.3
red 0.3

Accent Colors:

Hex Family Name Chroma
562C33 red darkslategray 20.4

B) Texture Analysis

Metric Value
Global Roughness 0.25
Mean Local Roughness 0.019
Roughness Uniformity 0.015
Edge Density 0.051
Mean Gradient Magnitude 0.12
Gradient Variance 0.026
Gradient Smoothness 0.0
Directional Coherence 0.011
Pattern Complexity 0.138
Pattern Repetition 1.0
Detail Frequency Ratio 0.659
Spatial Variation 0.167
Texture Consistency 0.736

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.545
Brightness Variance 0.25
Brightness Uniformity 0.54
Brightness Skewness -0.713
Brightness Entropy 7.199
Rms Contrast 0.25
Michelson Contrast 1.0
Weber Contrast 0.812
Mean Local Contrast 0.018
Contrast Uniformity 0.11
Dynamic Range 1.0
Effective Dynamic Range 0.769
Shadow Percentage 20.491
Midtone Percentage 29.51
Highlight Percentage 50.0
Shadow Clipping 0.173
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.022
Coarse Contrast 0.027
Multiscale Contrast Ratio 0.494
Edge Contrast 0.12
Contrast Clustering 0.264

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.746
Color Clustering 0.877
Color Transition Smoothness 0.692
Transition Uniformity 0.815
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.236
Saturation Variance 0.032
Low Saturation Ratio 0.795
Medium Saturation Ratio 0.173
High Saturation Ratio 0.033
Saturation Clustering 0.998
Hue Concentration 0.625
Complementary Balance 0.165
Analogous Dominance 0.787
Temperature Bias 0.65

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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2026). Ab7#11 - Research on Harmony - Variations 3 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1137.html
  2. [2] Quercy, A. (2025). ORCID https://orcid.org/0009-0000-2662-7790
  3. [3] Quercy, A. (2026). Computational Image Analysis Standard. https://ideamorphism.org/en/measurements/2025/11/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Where this work lives

Documented at

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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