AQC0861

Nanopublication — Computational Image Analysis - AQC0861

by Arnaud Quercy · F# Octaves - Reflexions 37 · 2025

Claim 1: Computational Image Analysis - AQC0861

The artwork F# Octaves [1] - Reflexions 37 (AQC0861) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-12-09. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 2334x3112 pixels. Analysis date: 2025-12-09.

Color Analysis

Rank	Hex	%	Family	Name
1	D9D5CD	30.0	white	lightgray
2	CFCBB9	24.0	yellow	silver
3	BDBBAD	14.4	yellow	steel gray
4	5B9491	7.6	green	cadetblue
5	A3A19C	7.0	gray	steel gray
6	397F7F	4.0	blue-green	seagreen
7	7AA9A5	3.9	green	lightslategray
8	202D2B	3.5	green	very dark gray
9	5B7F65	2.8	yellow-green	dimgray
10	355849	2.8	yellow-green	darkslategray
11	BF8F80	0.3	red-orange	rosybrown [Accent]

Color Families:

Family	%
yellow	38.4
white	30.0
green	15.0
gray	7.0
yellow-green	5.6
blue-green	4.0
red-orange	0.3

Accent Colors:

Hex	Family	Name	Chroma
BF8F80	red-orange	rosybrown	21.9

Texture Analysis

Metric	Value
Global Roughness	0.178
Mean Local Roughness	0.012
Roughness Uniformity	0.015
Edge Density	0.039
Mean Gradient Magnitude	0.104
Gradient Variance	0.027
Gradient Smoothness	0.0
Directional Coherence	0.036
Pattern Complexity	0.116
Pattern Repetition	1.0
Detail Frequency Ratio	0.601
Spatial Variation	0.119
Texture Consistency	0.544

Brightness & Contrast Analysis

Metric	Value
Mean Brightness	0.699
Brightness Variance	0.178
Brightness Uniformity	0.745
Brightness Skewness	-1.455
Brightness Entropy	6.833
Rms Contrast	0.178
Michelson Contrast	1.0
Weber Contrast	0.481
Mean Local Contrast	0.013
Contrast Uniformity	0.0
Dynamic Range	1.0
Effective Dynamic Range	0.553
Shadow Percentage	5.585
Midtone Percentage	24.289
Highlight Percentage	70.126
Shadow Clipping	0.002
Highlight Clipping	0.0
Tonal Balance	0.0
Fine Contrast	0.006
Medium Contrast	0.016
Coarse Contrast	0.028
Multiscale Contrast Ratio	0.228
Edge Contrast	0.104
Contrast Clustering	0.456

Spatial Distribution Analysis

Metric	Value
Spatial Coherence	0.726
Color Clustering	0.832
Color Transition Smoothness	0.735
Transition Uniformity	0.812
Sharp Transition Ratio	0.1
Transition Directionality	0.047
Mean Saturation	0.16
Saturation Variance	0.024
Low Saturation Ratio	0.818
Medium Saturation Ratio	0.175
High Saturation Ratio	0.006
Saturation Clustering	1.0
Hue Concentration	0.847
Complementary Balance	0.036
Analogous Dominance	0.898
Temperature Bias	-0.887

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 (2025). F# Octaves - Reflexions 37 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0861.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/f-octaves-reflexions-37_9j2.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 type	computational analysis
Voice	third person
Epistemic status	empirical measurement
Methodology	computational analysis
Certainty	high

Checksum (SHA-256)

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