Abstract
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.
Article PDF
Similar content being viewed by others
References
Adolphs, R., Gosselin, F., Buchanan, T. W., Tranel, D., Schyns, P. G., & Damasio, A. R. (2005). A mechanism for impaired fear recognition after amygdala damage. Nature, 433, 68–72.
Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613–621.
Bentin, S., Taylor, M. J., Rousselet, G. A., Itier, R. J., Caldara, R., Schyns, P. G., et al. (2007). Controlling interstimulus perceptual variance does not abolish N170 face sensitivity. Nature Neuroscience, 10, 801–802.
Bevilacqua, A., & Azzari, P. (2007). A high performance exact histogram specification algorithm. In R. Cucchiara (Ed.), 14th International Conference on Image Analysis and Processing (ICIAP’07) (pp. 623–628). Los Alamitos, CA: IEEE Computer Society Press.
Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433–436.
Campbell, F. W., & Robson, J. G. (1968). Application of Fourier analysis to the visibility of gratings. Journal of Physiology, 197, 551–566.
Carmel, D., & Bentin, S. (2002). Domain specificity versus expertise: Factors influencing distinct processing of faces. Cognition, 83, 1–29.
Chubb, C., Landy, M. S., & Econopouly, J. (2004). A visual mechanism tuned to black. Vision Research, 44, 3223–3232.
Coltuc, D., Bolon, P., & Chassery, J.-M. (2006). Exact histogram specification. IEEE Transactions on Image Processing, 15, 1143–1152.
Cornelissen, F. W., Peters, E. M., & Palmer, J. (2002). The Eyelink Toolbox: Eye tracking with MATLAB and the Psychophysics Toolbox. Behavior Research Methods, Instruments, & Computers, 34, 613–617.
Dakin, S. C., Hess, R. F., Ledgeway, T., & Achtman, R. L. (2002). What causes non-monotonic tuning of fMRI response to noisy images? Current Biology, 12, R476-R477.
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21.
De Valois, R. L., & De Valois, K. K. (1990). Spatial vision. New York: Oxford University Press.
Finkbeiner, M., & Palermo, R. (2009). The role of spatial attention in nonconscious processing: A comparison of face and nonface stimuli. Psychological Science, 20, 42–51.
Fiset, D., Blais, C., Gosselin, F., Bub, D., & Tanaka, J. (2008). Potent features for the categorization of Caucasian, African American, and Asian faces in Caucasian observers [Abstract]. Journal of Vision, 8(6), 258a.
Fründ, I., Busch, N. A., Körner, U., Schadow, J., & Herrmann, C. S. (2007). EEG oscillations in the gamma and alpha range respond differently to spatial frequency. Vision Research, 47, 2086–2098.
George, N., Jemel, B., Fiori, N., & Renault, B. (1997). Face and shape repetition effects in humans: A spatio-temporal ERP study. NeuroReport, 8, 1417–1423.
Gold, J. G., Bennett, P. J., & Sekuler, A. B. (1999). Identification of band-pass filtered letters and faces by human and ideal observers. Vision Research, 39, 3537–3560.
Hardee, J. E., Thompson, J. C., & Puce, A. (2008). The left amygdala knows fear: Laterality in the amygdala response to fearful eyes. Social Cognitive & Affective Neuroscience, 3, 47–54.
Hershler, O., & Hochstein, S. (2006). With a careful look: Still no low-level confound to face pop-out. Vision Research, 46, 3028–3035.
Honey, C., Kirchner, H., & VanRullen, R. (2008). Faces in the cloud: Fourier power spectrum biases ultrarapid face detection. Journal of Vision, 8(12, Art. 9), 1–13.
Itier, R. J., & Taylor, M. J. (2004). N170 or N1? Spatiotemporal differences between object and face processing using ERPs. Cerebral Cortex, 14, 132–142.
Ivry, R., & Robertson, L. C. (1998). The two sides of perception. Cambridge, MA: MIT Press.
Johannes, S., Münte, T. F., Heinze, H. J., & Mangun, G. R. (1995). Luminance and spatial attention effects on early visual processing. Cognitive Brain Research, 2, 189–205.
Knebel, J.-F., Toepel, U., Hudry, J., Le Coutre, J., & Murray, M. M. (2008). Generating controlled image sets in cognitive neuroscience research. Brain Topography, 20, 284–289.
Liang, X., Zebrowitz, L. A., & Aharon, I. (2009). Effective connectivity between amygdala and orbitofrontal cortex differentiates the perception of facial expressions. Social Neuroscience, 4, 185–196.
Liu, J., Harris, A., & Kanwisher, N. (2002). Stages of processing in face perception: An MEG study. Nature Neuroscience, 5, 910–916.
Loschky, L. C., Sethi, A., Simons, D. J., Pydimari, T. W., Ochs, D., & Corbeille, J. L. (2007). The importance of information localization in scene gist recognition. Journal of Experimental Psychology: Human Perception & Performance, 33, 1431–1450.
Luck, S. J. (2005). An introduction to the event-related potential technique. Cambridge, MA: MIT Press.
Mack, M. L., Gauthier, I., Sadr, J., & Palmeri, T. J. (2008). Object detection and basic-level categorization: Sometimes you know it is there before you know what it is. Psychological Bulletin & Review, 15, 28–35.
Metha, A. B., Vingrys, A. J., & Badcock, D. R. (1993). Calibration of a color monitor for visual psychophysics. Behavior Research Methods, Instruments, & Computers, 25, 371–383.
Morovic, J., Shaw, J., & Sun, P.-L. (2002). A fast, non-iterative and exact histogram matching algorithm. Pattern Recognition Letters, 23, 127–135.
Motoyoshi, I., Nishida, S., Sharan, L., & Adelson, E. H. (2007). Image statistics and the perception of surface qualities. Nature, 447, 206–209.
Olman, C., Boyaci, H., Fang, F., & Doerschner, K. (2008). V1 responses to different types of luminance histogram contrast [Abstract]. Journal of Vision, 8(6), 345a.
Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442.
Pelli, D. G., & Zhang, L. (1991). Accurate control of contrast on microcomputer displays. Vision Research, 31, 1337–1350.
Poynton, C. A. (1993). “Gamma” and its disguises: The nonlinear mappings of intensity in perception, CRTs, film and video. SMPTE Journal, 102, 1099–1108.
Rolland, J. P., Vo, V., Bloss, B., & Abbey, C. K. (2000). Fast algorithms for histogram matching: Application to texture synthesis. Journal of Electronic Imaging, 9, 39–45.
Rose, J., Otto, T., & Dittrich, L. (2008). The Biopsychology-Toolbox: A free, open-source Matlab-toolbox for the control of behavioral experiments. Journal of Neuroscience Methods, 175, 104–107.
Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Bruyer, R., Linotte, S., & Crommelinck, M. (2000). The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-specific processes in the human brain. NeuroReport, 11, 69–74.
Rousselet, G. A., Husk, J. S., Bennett, P. J., & Sekuler, A. B. (2008). Time course and robustness of ERP object and face differences. Journal of Vision, 8(12, Art. 3), 1–18.
Rousselet, G. A., Macé, M. J. M., Thorpe, S. J., & Fabre-Thorpe, M. (2007). Limits of event-related potential differences in tracking object processing speed. Journal of Cognitive Neuroscience, 19, 1241–1258.
Rousselet, G. A., Pernet, C. R., Bennett, P. J., & Sekuler, A. B. (2008). Parametric study of EEG sensitivity to phase noise during face processing. BMC Neuroscience, 9, 98.
Sadr, J., & Sinha, P. (2001). Exploring object perception with random image structure evolution. MIT Artificial Intelligence Laboratory Memo No. 2001-6.
Sadr, J., & Sinha, P. (2004). Object recognition and random image structure evolution. Cognitive Science, 28, 259–287.
Seeck, M., Michel, C. M., Mainwaring, N., Cosgrove, R., Blume, H., Ives, J., et al. (1997). Evidence for rapid face recognition from human scalp and intracranial electrodes. NeuroReport, 8, 2749–2754.
Stanislaw, H., & Olzak, L. A. (1990). Parametric methods for gamma and inverse gamma correction with extensions to halftoning. Behavior Research Methods, Instruments, & Computers, 22, 402–408.
Tanaka, J. W., & Curran, T. (2001). A neural basis for expert object recognition. Psychological Science, 12, 43–47.
Thierry, G., Martin, C. D., Downing, P., & Pegna, A. J. (2007a). Controlling for interstimulus perceptual variance abolishes N170 face selectivity. Nature Neuroscience, 10, 505–511.
Thierry, G., Martin, C. D., Downing, P. E., & Pegna, A. J. (2007b). Is the N170 sensitive to the human face or to several intertwined perceptual and conceptual factors? Nature Neuroscience, 10, 802–803.
VanRullen, R. (2006). On second glance: Still no high-level pop-out effect for faces. Vision Research, 46, 3017–3027.
VanRullen, R., & Thorpe, S. J. (2001). The time course of visual processing: From early perception to decision-making. Journal of Cognitive Neuroscience, 13, 454–461.
Wan, Y., & Shi, D. (2007). Joint exact histogram specification and image enhancement through the wavelet transform. IEEE Transactions on Image Processing, 16, 2245–2250.
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2003). The SSIM index for image quality assessment. Retrieved January 27, 2010 from www.ece.uwaterloo.ca/~z70wang/research/ssim/
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600–612.
Willenbockel, V., Fiset, D., Chauvin, A., Blais, C. Arguin, M., Tanaka, J. W., et. al (2010). Does face inversion change spatial frequency tuning? Journal of Experimental Psychology: Human Perception & Performance, 36, 122–135.
Willenbockel, V., Fiset, D., & Tanaka, J. W. (2008, June). The role of luminance and facial features in race categorization. Poster presented at the North West Cognition and Memory 10th Annual Meeting, Seattle.
Williams, N. R., Willenbockel, V., & Gauthier, I. (2009). Sensitivity to spatial frequency and orientation content is not specific to face perception. Vision Research, 49, 2353–2362.
Xu, Y., Liu, J., & Kanwisher, N. (2005). The M170 is selective for faces, not for expertise. Neuropsychologia, 43, 588–597.
Yin, R. K. (1969). Looking at upside-down faces. Journal of Experimental Psychology, 81, 141–145.
Zion-Golumbic, E., Golan, T., Anaki, D., & Bentin, S. (2008). Human face preference in gamma-frequency EEG activity. NeuroImage, 39, 1980–1987.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Willenbockel, V., Sadr, J., Fiset, D. et al. Controlling low-level image properties: The SHINE toolbox. Behavior Research Methods 42, 671–684 (2010). https://doi.org/10.3758/BRM.42.3.671
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/BRM.42.3.671