AQC0919 | NAN-COL000036

Nanopublication — Computational Image Analysis - AQC0919

B Minor - Research on Harmony - Variations 8

Claim 1: Computational Image Analysis - AQC0919

Analysis record: B Minor - Research on Harmony - Variations 8 (AQC0919) [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: 2036x2036 pixels. Analysis date: 2025-12-11.

A) Color Analysis

Rank Color Hex % Family Name
1 9EAD8B 17.4 yellow-green darkseagreen
2 AFBE9C 16.3 yellow-green steel gray
3 CFE658 15.1 yellow-green ochre
4 5E5943 13.0 yellow dark brown
5 726E57 12.3 yellow dimgray
6 B8D125 7.4 yellow-green yellowgreen
7 889070 6.5 yellow-green gray
8 91D16F 6.1 yellow-green lightgreen
9 E7E2CA 5.0 yellow gainsboro
10 1B200A 0.9 yellow-green very dark gray
11 FEF8EB 0.3 yellow-orange white [Accent]
12 3A332B 0.3 orange darkslategray [Accent]

Color Families:

Family %
yellow-green 69.7
yellow 30.3
yellow-orange 0.3
orange 0.3

Accent Colors:

Hex Family Name Chroma
FEF8EB yellow-orange white 7.0
3A332B orange darkslategray 6.3

B) Texture Analysis

Metric Value
Global Roughness 0.173
Mean Local Roughness 0.024
Roughness Uniformity 0.019
Edge Density 0.15
Mean Gradient Magnitude 0.197
Gradient Variance 0.046
Gradient Smoothness 0.0
Directional Coherence 0.004
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.637
Spatial Variation 0.126
Texture Consistency 0.604

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.627
Brightness Variance 0.173
Brightness Uniformity 0.724
Brightness Skewness -0.59
Brightness Entropy 7.195
Rms Contrast 0.173
Michelson Contrast 1.0
Weber Contrast 0.557
Mean Local Contrast 0.027
Contrast Uniformity 0.251
Dynamic Range 0.996
Effective Dynamic Range 0.529
Shadow Percentage 4.552
Midtone Percentage 41.676
Highlight Percentage 53.772
Shadow Clipping 0.012
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.033
Coarse Contrast 0.044
Multiscale Contrast Ratio 0.292
Edge Contrast 0.197
Contrast Clustering 0.396

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.768
Color Clustering 0.373
Color Transition Smoothness 0.505
Transition Uniformity 0.678
Sharp Transition Ratio 0.1
Transition Directionality 0.004
Mean Saturation 0.344
Saturation Variance 0.046
Low Saturation Ratio 0.639
Medium Saturation Ratio 0.285
High Saturation Ratio 0.075
Saturation Clustering 0.999
Hue Concentration 0.948
Complementary Balance 0.0
Analogous Dominance 0.997
Temperature Bias 0.34

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). B Minor - Research on Harmony - Variations 8 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0919.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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