AQC0529 | NAN-COL000406

Nanopublication — Computational Image Analysis - AQC0529

D Major9 - Research on Harmony - Variation 9

Claim 1: Computational Image Analysis - AQC0529

K-means clustering (10 colors) performed on artwork D Major9 - Research on Harmony - Variation 9 (AQC0529) [1] by Arnaud Quercy [2] on 2025-10-03, 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: 1760x2346 pixels. Analysis date: 2025-10-03.

A) Color Analysis

Rank Color Hex % Family Name
1 E6E4DE 23.8 white white
2 DDD9D2 12.5 white gainsboro
3 D5B89C 11.0 orange tan
4 C9A784 10.7 orange burlywood
5 BA886D 9.2 orange rosybrown
6 A6704F 8.9 orange indianred
7 535152 7.7 gray darkslategray
8 BD9752 7.3 yellow-orange peru
9 7E7975 4.9 gray gray
10 9C9690 3.9 gray steel gray
11 33292B 0.3 red very dark gray [Accent]
12 372926 0.3 red-orange very dark gray [Accent]

Color Families:

Family %
orange 39.8
white 36.3
gray 16.5
yellow-orange 7.3
red 0.3
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
33292B red very dark gray 6.0
372926 red-orange very dark gray 6.4

B) Texture Analysis

Metric Value
Global Roughness 0.181
Mean Local Roughness 0.021
Roughness Uniformity 0.02
Edge Density 0.12
Mean Gradient Magnitude 0.169
Gradient Variance 0.042
Gradient Smoothness 0.0
Directional Coherence 0.016
Pattern Complexity 0.133
Pattern Repetition 1.0
Detail Frequency Ratio 0.653
Spatial Variation 0.069
Texture Consistency 0.708

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.689
Brightness Variance 0.181
Brightness Uniformity 0.737
Brightness Skewness -0.466
Brightness Entropy 6.994
Rms Contrast 0.181
Michelson Contrast 0.969
Weber Contrast 0.498
Mean Local Contrast 0.023
Contrast Uniformity 0.089
Dynamic Range 0.984
Effective Dynamic Range 0.561
Shadow Percentage 4.098
Midtone Percentage 40.067
Highlight Percentage 55.835
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.012
Medium Contrast 0.028
Coarse Contrast 0.039
Multiscale Contrast Ratio 0.297
Edge Contrast 0.169
Contrast Clustering 0.292

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.733
Color Clustering 0.735
Color Transition Smoothness 0.58
Transition Uniformity 0.733
Sharp Transition Ratio 0.1
Transition Directionality 0.02
Mean Saturation 0.22
Saturation Variance 0.041
Low Saturation Ratio 0.66
Medium Saturation Ratio 0.339
High Saturation Ratio 0.001
Saturation Clustering 1.0
Hue Concentration 0.992
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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2024). D Major9 - Research on Harmony - Variation 9 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0529.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

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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