Controlling low-level image properties: The SHINE toolbox
Open accessAbstract
Visual perception can be influenced by top-down processes related to the observer's goals and expectations, as well as by bottom-up processes related to low-level stimulus attributes, such as luminance, contrast, and spatial frequency. When using different physical stimuli across psychological conditions, one faces the problem of disentangling the contributions of low- and high-level factors. Here, we make available the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB, which we have found useful for controlling a number of image properties separately or simultaneously. The toolbox features functions for specifying the (rotational average of the) Fourier amplitude spectra, for normalizing and scaling mean luminance and contrast, and for exact histogram specification optimized for perceptual visual quality. SHINE can thus be employed for parametrically modifying a number of image properties or for equating them across stimuli to minimize potential low-level confounds in studies on higher level processes.
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Task-irrelevant face and hand stimuli were matched for luminance across conditions using the SHINE toolbox.
“Task-irrelevant stimuli (i.e., face and hand stimuli) were matched for luminance across conditions using the SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization)toolbox (Willenbockel et al., 2010) for Matlab 8.3 (TheMathworks, Inc., MA).”
From Interplay of self-other distinction and cognitive control mechanisms in a social automatic imitation task: An ERP study (2021) · cites this paper as Willenbockel et al., 2010
Hand stimuli were matched for luminance using the SHINE toolbox to avoid perceptual differences or attentional effects driven by differential luminance characteristics of the ethnically diverse hand wrists.
“Moreover, in order to avoid perceptual differences or attentional effects driven by differential luminance characteristics of the ethnically diverse hand wrists, hand stimuli were matched for luminance using the SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization) toolbox56 for Matlab 8.3 (TheMathworks, Inc., MA).”
From Event-related potentials of automatic imitation are modulated by ethnicity during stimulus processing, but not during motor execution (2018) · cites this paper as [56]
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“Low-level image properties were equalized by the SHINE toolbox [50] (The toolbox features functions for specifying the (rotational average of the) Fourier amplitude spectra, for normalizing and scaling mean luminance and contrast, and for exact histogram speci fi cation optimized for perceptual…”
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“Stefanics et al. (2019) took the faces from the Radboud Faces Database ( Langner et al., 2010) and processed them with the SHINE toolbox ( Willenbockel et al., 2010).”
- Authors
- Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W.
- Affiliations
- Université de Montréal (CA)
- University of Massachusetts Boston (US)
- University of Victoria (CA)
- Corresponding author
- Willenbockel, Verena
- Venue
- Behavior Research Methods
- Published
- 2010-08
- Volume
- 42
- Issue
- 3
- Pages
- 671-684
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2013137720
- Cited by
- 1,166
- References
- 63
- Citations by year
- 2026: 88 · 2025: 112 · 2024: 108 · 2023: 96 · 2022: 101 · 2021: 111 · 2020: 100 · 2019: 91 · 2018: 90 · 2017: 74 · 2016: 68 · 2015: 47 · 2014: 44 · 2013: 16 · 2012: 8
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Université de Montréal
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- image normalization, histogram matching, Fourier amplitude spectra, contrast normalization, visual perception, top-down and bottom-up processing, MATLAB toolbox
- MeSH terms
- Algorithms, Calibration, Fourier Analysis, Humans, Pattern Recognition, Visual, Photic Stimulation, Reference Values, Software, Contrast Sensitivity
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