recolorize

recolorize performs flexible color segmentation of biological images in R, assigning pixels to shared predefined color classes to support analyses in disease ecology and speciation dynamics.

Key Features:

• Human-Subjective Color Segmentation: Implements human-subjective methods to classify pixels into predefined color classes shared across multiple images.
• Batch Processing Capabilities: Provides functions for batch-processing low-variation image sets to ensure consistent classification across large datasets.
• Handling High Variation Sources: Includes tools to manage images from diverse sources exhibiting high technical variation.
• Export Options: Supports export in various formats for interoperability with other color analysis packages.
• Integration with Reflectance Spectra: Combines color segmentation with reflectance spectra analysis to refine interpretation of color patterns.

Scientific Applications:

• Disease Ecology: Enables identification of phenotypic variations linked to pathogen presence or resistance by extracting precise color patterns.
• Speciation Dynamics: Facilitates analysis of morphological color differences indicative of evolutionary divergence.

Methodology:

Assigns pixels to specific color classes shared across a dataset using a dual approach of automated processing supplemented by human-subjective input, and integrates reflectance spectra to refine segmentation for both low-variation batch and high-variation image sources.