Controlling low-level image properties: The SHINE toolbox

By Verena Willenbockel, Javid Sadr, D. Fiset, Greg O. Horne, F. Gosselin, James W. Tanaka. Behavior Research Methods, 2010

1,047 citations recorded by the source corpus.

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Recurrent Processing during Object Recognition (2013, methodology): “The images were processed with the SHINE toolbox (Willenbockel et al., 2010) to convert their colorspace to grayscale and to normalize luminance across categories.”
The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded (2012, methodology): “The images were processed with the SHINE toolbox (Willenbockel et al., 2010) to convert their colorspace to grayscale and to normalize luminance across categories.”
Hierarchical Reconfiguration of Neurocognitive Task Set Representations Mediates Cognitive Flexibility (2026, unknown): “To determine which response rule to use on a given trial, participants had to integrate three sources of information associated with three perceptual features of the cue object: texture, shape, and color ( Figure 1A ).”
Correlations of neural predictability and information transfer in cortex and their relation to predictive coding (2025, unknown): “5.3, Singular Inversions), converted to black and white and luminance histogram equalized using SHINE [15].”
Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception (2025, unknown): “All stimuli were gray-scaled and scaled for equal luminance and size using the SHINE toolbox 34 .”