AQC0557

Nanopublication — Computational Image Analysis - AQC0557

Claim 1: Computational Image Analysis - AQC0557

Analysis record [3]: C Major9 - Research [1] on Harmony - Variation 9 (AQC0557) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-02-04.

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]: 1027x1369 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 575870 17.4 violet dusty mauve
2 444761 16.1 violet dusty mauve
3 6C6980 13.7 violet dusty mauve
4 CB503A 11.5 red-orange indianred
5 303451 11.0 violet dusty mauve
6 DBD4D2 9.5 white lightgray
7 848196 5.7 violet dusty mauve
8 D29A91 5.6 red-orange rosybrown
9 C1C0C2 5.0 gray silver
10 181B37 4.5 violet very dark purple
11 A86C4E 0.3 orange indianred [Accent]
12 250A11 0.3 red very dark red [Accent]
13 9DA9AF 0.3 blue steel gray [Accent]

Color Families:

Family %
violet 68.3
red-orange 17.2
white 9.5
gray 5.0
orange 0.3
red 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
A86C4E orange indianred 34.2
250A11 red very dark red 15.1
9DA9AF blue steel gray 5.8

Texture Analysis

Metric Value
Global Roughness 0.203
Mean Local Roughness 0.057
Roughness Uniformity 0.033
Edge Density 0.276
Mean Gradient Magnitude 0.398
Gradient Variance 0.109
Gradient Smoothness 0.17
Directional Coherence 0.006
Pattern Complexity 0.131
Pattern Repetition 1.0
Detail Frequency Ratio 0.707
Spatial Variation 0.143
Texture Consistency 0.619

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.433
Brightness Variance 0.203
Brightness Uniformity 0.531
Brightness Skewness 0.644
Brightness Entropy 7.427
Rms Contrast 0.203
Michelson Contrast 1.0
Weber Contrast 0.739
Mean Local Contrast 0.056
Contrast Uniformity 0.452
Dynamic Range 1.0
Effective Dynamic Range 0.682
Shadow Percentage 33.638
Midtone Percentage 48.472
Highlight Percentage 17.89
Shadow Clipping 0.007
Highlight Clipping 0.0
Tonal Balance 0.105
Fine Contrast 0.036
Medium Contrast 0.069
Coarse Contrast 0.081
Multiscale Contrast Ratio 0.45
Edge Contrast 0.398
Contrast Clustering 0.381

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.756
Color Clustering 0.741
Color Transition Smoothness 0.0
Transition Uniformity 0.166
Sharp Transition Ratio 0.1
Transition Directionality 0.012
Mean Saturation 0.318
Saturation Variance 0.047
Low Saturation Ratio 0.569
Medium Saturation Ratio 0.327
High Saturation Ratio 0.104
Saturation Clustering 0.994
Hue Concentration 0.558
Complementary Balance 0.0
Analogous Dominance 0.716
Temperature Bias -0.153

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 (2024). C Major9 - Research on Harmony - Variation 9 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0557.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-major9-research-on-harmony-variation-9_68u.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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