AQC0341

Nanopublication — Computational Image Analysis - AQC0341

by Arnaud Quercy · Blindsight · 2022

Claim 1: Computational Image Analysis - AQC0341

Analysis record [3]: Blindsight [1] (AQC0341) [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]: 2000x2000 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 07080A 48.5 black black
2 0E0F11 19.2 black black
3 1C1710 8.1 orange black
4 191C20 6.8 gray very dark gray
5 282A2C 4.9 gray very dark gray
6 362B1C 4.7 orange very dark gray
7 41413E 3.7 gray darkslategray
8 56451D 2.3 yellow-orange dark brown
9 5A5F59 1.0 gray dimgray
10 7A7028 0.8 yellow olivedrab

Color Families:

Family %
black 67.7
gray 16.4
orange 12.8
yellow-orange 2.3
yellow 0.8

Texture Analysis

Metric Value
Global Roughness 0.077
Mean Local Roughness 0.002
Roughness Uniformity 0.004
Edge Density 0.003
Mean Gradient Magnitude 0.015
Gradient Variance 0.002
Gradient Smoothness 0.0
Directional Coherence 0.531
Pattern Complexity 0.067
Pattern Repetition 1.0
Detail Frequency Ratio 0.576
Spatial Variation 0.053
Texture Consistency 0.317

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.082
Brightness Variance 0.077
Brightness Uniformity 0.056
Brightness Skewness 2.087
Brightness Entropy 5.397
Rms Contrast 0.077
Michelson Contrast 1.0
Weber Contrast 0.854
Mean Local Contrast 0.002
Contrast Uniformity 0.0
Dynamic Range 0.502
Effective Dynamic Range 0.235
Shadow Percentage 98.293
Midtone Percentage 1.707
Highlight Percentage 0.0
Shadow Clipping 0.046
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.001
Medium Contrast 0.003
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.015
Contrast Clustering 0.683

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.774
Color Clustering 0.309
Color Transition Smoothness 0.949
Transition Uniformity 0.982
Sharp Transition Ratio 0.1
Transition Directionality 0.546
Mean Saturation 0.315
Saturation Variance 0.029
Low Saturation Ratio 0.38
Medium Saturation Ratio 0.607
High Saturation Ratio 0.013
Saturation Clustering 1.0
Hue Concentration 0.391
Complementary Balance 0.142
Analogous Dominance 0.687
Temperature Bias -0.418

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). Blindsight — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0341.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2022/01/blindsight_3wu.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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