AQC0390 | NAN-COL000496

Nanopublication — Computational Image Analysis - AQC0390

by Arnaud Quercy · The driver of Tram 28, Lisbon · 2022
The driver of Tram 28, Lisbon

Claim 1: Computational Image Analysis - AQC0390

K-means clustering (10 colors) performed on artwork The driver of Tram 28, Lisbon (AQC0390) [1] by Arnaud Quercy [2] on 2025-10-03, according to IDS-CMP-2025 [3]. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

Context

Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 1536x2048 pixels. Analysis date: 2025-10-03.

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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2022). The driver of Tram 28, Lisbon - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0390.html
  2. [2] Quercy, A. (2025). ORCID https://orcid.org/0009-0000-2662-7790
  3. [3] Quercy, A. (2026). Computational Image Analysis Standard. https://ideamorphism.org/en/measurements/2025/09/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

6d43d33e3d384835e9296173098d99fd9c15e2961fb923fd02785ab1f0dcf6ff

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