AQC0894 | NAN-COL000066

Nanopublication — Computational Image Analysis - AQC0894

E Minor - Research on Harmony - Variations 7

Claim 1: Computational Image Analysis - AQC0894

Analysis record: E Minor - Research on Harmony - Variations 7 (AQC0894) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2025-12-11.

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: 2061x3092 pixels. Analysis date: 2025-12-11.

A) Color Analysis

Rank Color Hex % Family Name
1 EBA757 27.2 orange sandybrown
2 EBB26E 13.9 orange burlywood
3 E59B40 12.8 orange peru
4 EAE1C2 12.7 yellow wheat
5 D69B5A 10.2 orange darksalmon
6 B9D45E 9.7 yellow-green ochre
7 534E45 4.1 yellow-orange dark brown
8 DEE163 4.0 yellow khaki
9 ABBA2B 3.3 yellow yellowgreen
10 372E1D 2.1 yellow-orange darkslategray
11 2D0D04 0.3 red-orange very dark red [Accent]

Color Families:

Family %
orange 64.1
yellow 20.0
yellow-green 9.7
yellow-orange 6.2
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
2D0D04 red-orange very dark red 19.7

B) Texture Analysis

Metric Value
Global Roughness 0.136
Mean Local Roughness 0.018
Roughness Uniformity 0.021
Edge Density 0.082
Mean Gradient Magnitude 0.146
Gradient Variance 0.053
Gradient Smoothness 0.0
Directional Coherence 0.014
Pattern Complexity 0.111
Pattern Repetition 1.0
Detail Frequency Ratio 0.633
Spatial Variation 0.061
Texture Consistency 0.485

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.699
Brightness Variance 0.136
Brightness Uniformity 0.806
Brightness Skewness -1.833
Brightness Entropy 6.384
Rms Contrast 0.136
Michelson Contrast 1.0
Weber Contrast 0.268
Mean Local Contrast 0.02
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.58
Shadow Percentage 5.744
Midtone Percentage 17.073
Highlight Percentage 77.183
Shadow Clipping 0.0
Highlight Clipping 0.002
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.025
Coarse Contrast 0.036
Multiscale Contrast Ratio 0.272
Edge Contrast 0.146
Contrast Clustering 0.515

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.766
Color Clustering 0.425
Color Transition Smoothness 0.641
Transition Uniformity 0.649
Sharp Transition Ratio 0.1
Transition Directionality 0.02
Mean Saturation 0.535
Saturation Variance 0.036
Low Saturation Ratio 0.169
Medium Saturation Ratio 0.733
High Saturation Ratio 0.098
Saturation Clustering 0.999
Hue Concentration 0.963
Complementary Balance 0.0
Analogous Dominance 0.999
Temperature Bias 0.818

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. (2025). E Minor - Research on Harmony - Variations 7 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0894.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/2025/09/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

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