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Controlling low-level image properties: the SHINE toolbox
Verena Willenbockel1, Javid Sadr, Daniel Fiset
1Département de Psychologie, Université de Montréal, Montréal, Québec, Canada. verena.willenbockel@umontreal.ca
Researchers developed the SHINE toolbox to control image properties like spectrum and contrast. This helps separate low-level visual factors from high-level cognitive influences in perception studies.
Area of Science:
- Visual perception research
- Computational neuroscience
- Image processing
Background:
- Visual perception is influenced by both top-down (goals, expectations) and bottom-up (luminance, contrast, spatial frequency) processes.
- Disentangling low-level stimulus attributes from high-level cognitive factors is a challenge in visual science.
- Controlling physical stimulus properties is crucial for isolating perceptual mechanisms.
Purpose of the Study:
- To introduce the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB.
- To provide a method for controlling and equating image properties across different experimental conditions.
- To minimize low-level confounds in studies investigating higher-level visual processing.
Main Methods:
- The SHINE toolbox offers functions to specify Fourier amplitude spectra.
- It enables normalization and scaling of mean luminance and contrast.
- The toolbox allows for precise histogram specification optimized for visual quality.
Main Results:
- SHINE provides parametric control over multiple image properties simultaneously or individually.
- The toolbox facilitates the equalization of image characteristics across stimuli.
- This enables researchers to minimize confounds arising from low-level stimulus variations.
Conclusions:
- The SHINE toolbox is a valuable resource for visual perception research.
- It aids in the systematic manipulation and control of image properties.
- By minimizing low-level confounds, SHINE supports the investigation of higher-level visual processes.
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