AQC0388

Nanopublication — Computational Image Analysis - AQC0388

by Arnaud Quercy · Morning cuddle · 2022

Claim 1: Computational Image Analysis - AQC0388

Computational image analysis [3] of artwork Morning [1] cuddle (AQC0388) [2] by Arnaud Quercy [2] using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 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]: 1536x2048 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank	Hex	%	Family	Name
1	22242B	20.8	blue-violet	very dark gray
2	393C42	17.6	gray	grayish purple
3	C89C8E	12.8	orange	rosybrown
4	D8D4CE	9.5	white	lightgray
5	9D7E7C	9.4	red-orange	gray
6	1D4468	7.8	blue-violet	grayish purple
7	EBBE6F	7.4	yellow-orange	burlywood
8	885543	5.7	orange	burnt sienna
9	50616E	5.6	blue	grayish purple
10	D96030	3.3	orange	chocolate
11	ACC1C6	0.3	blue-green	silver [Accent]

Color Families:

Family	%
blue-violet	28.6
orange	21.8
gray	17.6
white	9.5
red-orange	9.4
yellow-orange	7.4
blue	5.6
blue-green	0.3

Accent Colors:

Hex	Family	Name	Chroma
ACC1C6	blue-green	silver	7.8

Texture Analysis

Metric	Value
Global Roughness	0.247
Mean Local Roughness	0.033
Roughness Uniformity	0.023
Edge Density	0.203
Mean Gradient Magnitude	0.25
Gradient Variance	0.06
Gradient Smoothness	0.017
Directional Coherence	0.024
Pattern Complexity	0.104
Pattern Repetition	1.0
Detail Frequency Ratio	0.639
Spatial Variation	0.116
Texture Consistency	0.695

Brightness & Contrast Analysis

Metric	Value
Mean Brightness	0.421
Brightness Variance	0.247
Brightness Uniformity	0.414
Brightness Skewness	0.406
Brightness Entropy	7.609
Rms Contrast	0.247
Michelson Contrast	1.0
Weber Contrast	0.817
Mean Local Contrast	0.035
Contrast Uniformity	0.326
Dynamic Range	1.0
Effective Dynamic Range	0.718
Shadow Percentage	48.234
Midtone Percentage	29.092
Highlight Percentage	22.673
Shadow Clipping	0.001
Highlight Clipping	0.003
Tonal Balance	0.279
Fine Contrast	0.018
Medium Contrast	0.043
Coarse Contrast	0.049
Multiscale Contrast Ratio	0.359
Edge Contrast	0.25
Contrast Clustering	0.305

Spatial Distribution Analysis

Metric	Value
Spatial Coherence	0.716
Color Clustering	0.669
Color Transition Smoothness	0.34
Transition Uniformity	0.598
Sharp Transition Ratio	0.1
Transition Directionality	0.028
Mean Saturation	0.335
Saturation Variance	0.049
Low Saturation Ratio	0.533
Medium Saturation Ratio	0.386
High Saturation Ratio	0.081
Saturation Clustering	0.998
Hue Concentration	0.156
Complementary Balance	0.232
Analogous Dominance	0.545
Temperature Bias	0.103

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 (2022). Morning cuddle — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0388.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2022/01/morning-cuddle_4f4.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/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)

1c3e259149ecb93a20ed8e81520c9f855c2a13a99697dccadef7c554c0df0a70