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Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

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Parametric fuzzy sets for automatic color naming.

Robert Benavente1, Maria Vanrell, Ramon Baldrich

  • 1Computer Vision Center, Campus UAB, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain. robert@cvc.uab.es

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|October 3, 2008
PubMed
Summary
This summary is machine-generated.

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This study introduces a parametric model for automatic color naming using fuzzy sets and psychophysical data. The model accurately assigns color names, offering computational advantages for computer vision applications.

Area of Science:

  • Computer Vision
  • Color Science
  • Computational Intelligence

Background:

  • Automatic color naming is crucial for image analysis.
  • Existing models may lack flexibility or efficiency.
  • Parametric models offer potential advantages in implementation and analysis.

Purpose of the Study:

  • To develop a novel parametric model for automatic color naming.
  • To represent color categories as fuzzy sets with parametric membership functions.
  • To validate the model using psychophysical experimental data.

Main Methods:

  • A parametric model representing color categories as fuzzy sets.
  • Estimation of model parameters via a fitting process.
  • Utilizing data from psychophysical experiments for parameter training.

Related Experiment Videos

Last Updated: Jun 29, 2026

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
09:46

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

Published on: August 19, 2013

Main Results:

  • The model's name assignments align with established psychophysical findings.
  • Demonstrated agreement with human color perception data.
  • The parametric approach proved effective in color categorization.

Conclusions:

  • The proposed parametric model provides accurate automatic color naming.
  • Offers significant advantages for computer vision tasks (implementation, data handling, analysis, updating).
  • High-level color-naming information is valuable for diverse applications.