AQC0449 | NAN-COL000459

Nanopublication — Computational Image Analysis - AQC0449

Lone Star State

Claim 1: Computational Image Analysis - AQC0449

Analysis record: Lone Star State (AQC0449) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2025-10-03.

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.

A) Color Analysis

Rank Color Hex % Family Name
1 D7D0C6 17.2 yellow-orange lightgray
2 D8BF9D 14.1 yellow-orange tan
3 2F2820 11.8 orange very dark gray
4 4D3D32 11.0 orange darkslategray
5 C9A56C 9.7 yellow-orange ochre
6 585761 9.5 violet dusty mauve
7 9FAEA3 9.1 yellow-green steel gray
8 88867A 7.9 yellow gray
9 833D2B 6.0 red-orange russet
10 B76A44 3.7 orange peru
11 4F5F8C 0.3 blue-violet grayish purple [Accent]
12 7AC6A9 0.3 green mediumaquamarine [Accent]
13 B68C94 0.3 red rosybrown [Accent]
14 29434E 0.3 blue darkslategray [Accent]

Color Families:

Family %
yellow-orange 41.0
orange 26.6
violet 9.5
yellow-green 9.1
yellow 7.9
red-orange 6.0
blue-violet 0.3
green 0.3
red 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
4F5F8C blue-violet grayish purple 27.9
7AC6A9 green mediumaquamarine 30.8
B68C94 red rosybrown 17.1
29434E blue darkslategray 12.2

B) Texture Analysis

Metric Value
Global Roughness 0.241
Mean Local Roughness 0.019
Roughness Uniformity 0.022
Edge Density 0.072
Mean Gradient Magnitude 0.154
Gradient Variance 0.059
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.116
Pattern Repetition 1.0
Detail Frequency Ratio 0.607
Spatial Variation 0.133
Texture Consistency 0.795

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.533
Brightness Variance 0.241
Brightness Uniformity 0.549
Brightness Skewness -0.227
Brightness Entropy 7.582
Rms Contrast 0.241
Michelson Contrast 1.0
Weber Contrast 0.761
Mean Local Contrast 0.02
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.682
Shadow Percentage 30.147
Midtone Percentage 29.095
Highlight Percentage 40.759
Shadow Clipping 0.001
Highlight Clipping 0.002
Tonal Balance 0.299
Fine Contrast 0.01
Medium Contrast 0.026
Coarse Contrast 0.041
Multiscale Contrast Ratio 0.23
Edge Contrast 0.154
Contrast Clustering 0.205

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.683
Color Clustering 0.81
Color Transition Smoothness 0.594
Transition Uniformity 0.609
Sharp Transition Ratio 0.1
Transition Directionality 0.014
Mean Saturation 0.292
Saturation Variance 0.041
Low Saturation Ratio 0.614
Medium Saturation Ratio 0.336
High Saturation Ratio 0.05
Saturation Clustering 0.999
Hue Concentration 0.738
Complementary Balance 0.07
Analogous Dominance 0.852
Temperature Bias 0.718

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. (2023). Lone Star State - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0449.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

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