AQC0677

Nanopublication — Computational Image Analysis - AQC0677

by Arnaud Quercy · C Octaves - Reflexions 13 · 2024

Claim 1: Computational Image Analysis - AQC0677

Analysis record [3]: C Octaves [1] - Reflexions 13 (AQC0677) [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]: 2379x3568 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 D1C2BE 23.6 red-orange silver
2 D8D4D5 18.1 white lightgray
3 ADAFB1 13.8 gray steel gray
4 D0ACA3 10.5 red-orange tan
5 959799 9.5 gray steel gray
6 BD6D46 6.8 orange peru
7 A15236 6.1 orange burnt sienna
8 CB8D73 4.7 orange rosybrown
9 7C706F 3.8 gray dimgray
10 3B383A 3.1 gray dusty mauve
11 171B24 0.3 blue-violet very dark gray [Accent]

Color Families:

Family %
red-orange 34.2
gray 30.2
white 18.1
orange 17.6
blue-violet 0.3

Accent Colors:

Hex Family Name Chroma
171B24 blue-violet very dark gray 7.0

Texture Analysis

Metric Value
Global Roughness 0.155
Mean Local Roughness 0.016
Roughness Uniformity 0.015
Edge Density 0.067
Mean Gradient Magnitude 0.134
Gradient Variance 0.031
Gradient Smoothness 0.0
Directional Coherence 0.016
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.607
Spatial Variation 0.094
Texture Consistency 0.699

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.674
Brightness Variance 0.155
Brightness Uniformity 0.771
Brightness Skewness -1.1
Brightness Entropy 7.046
Rms Contrast 0.155
Michelson Contrast 1.0
Weber Contrast 0.462
Mean Local Contrast 0.017
Contrast Uniformity 0.072
Dynamic Range 1.0
Effective Dynamic Range 0.471
Shadow Percentage 3.13
Midtone Percentage 34.447
Highlight Percentage 62.422
Shadow Clipping 0.002
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.021
Coarse Contrast 0.034
Multiscale Contrast Ratio 0.248
Edge Contrast 0.134
Contrast Clustering 0.301

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.717
Color Clustering 0.601
Color Transition Smoothness 0.662
Transition Uniformity 0.791
Sharp Transition Ratio 0.1
Transition Directionality 0.019
Mean Saturation 0.18
Saturation Variance 0.043
Low Saturation Ratio 0.804
Medium Saturation Ratio 0.164
High Saturation Ratio 0.032
Saturation Clustering 1.0
Hue Concentration 0.974
Complementary Balance 0.005
Analogous Dominance 0.987
Temperature Bias 0.978

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 Octaves - Reflexions 13 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0677.html

[2] Quercy, A. (2024). C Octaves - Reflexions 13 - Gallery. https://artquamanima.com/en/artworks/2024/01/c-octaves-reflexions-13_7ji.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 typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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