Data & Code
The Mid-Level Vision Toolbox is used for computing mid-level vision properties and manipulating images.
Walther, D. B., & Shen, D. (2014). Nonaccidental properties underlie human categorization of complex natural scenes. Psychological science, 25(4), 851-860. https://doi.org/10.1177/0956797613512662.
Rezanejad, M., Downs, G., Wilder, J., Walther, D. B., Jepson, A., Dickinson, S., & Siddiqi, K. (2019). Scene categorization from contours: Medial axis based salience measures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4116-4124).
The SaliencyToolbox is used for computing the saliency map of an image.
Walther, D., & Koch, C. (2006). Modeling attention to salient proto-objects. Neural networks, 19(9), 1395-1407. https://doi.org/10.1016/j.neunet.2006.10.001.
A circular space of realistic scenes, generated by a generative adversarial network.
Son, G., Walther, D. B., & Mack, M. L. (2022). Scene wheels: measuring perception and memory of real-world scenes with a continuous stimulus space. Behavior Research Methods, 54(1), 444-456. https://doi.org/10.3758/s13428-021-01630-5
Scripts for spatial frequency filtering of images.
Sabrina Perfetto, John Wilder, and Dirk B. Walther (2020) Effects of spatial frequency filtering choices on the perception of filtered images, Vision 4(2), 29. doi: https://doi.org/10.3390/vision4020029
Toronto Scenes is a set of 475 color photographs and line drawings of six natural scene categories which have been used in a number of publications. Please cite:
Walther DB, Chai B, Caddigan E, Beck DM, & Fei-Fei L. (2011). Simple line drawings suffice for functional MRI decoding of natural scene categories, PNAS. 108(23): 9661-9666.
Torralbo A, Walther DB, Chai B, Caddigan E, Fei-Fei L, Beck DM. (2013). Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity. PLoS ONE. 8(3): e58594. doi: 10.1371/journal.pone.0058594