# materialmodifier: An R package of photo editing effects for material perception research

## TL;DR

This paper introduces an R package for editing photos to manipulate how materials look, helping researchers study how people perceive surfaces and appearances.

## Contribution

The package provides a reproducible and quantitative method for manipulating material appearance in images for psychological research.

## Key findings

- The package implements an image-processing algorithm to control surface properties like gloss and roughness.
- A behavioral evaluation experiment demonstrated the package's effectiveness in material perception research.
- The package is available on CRAN with documentation and source code on GitHub.

## Abstract

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier.