Texture Tiling Model for Human Perception At a Glance https://hdl.handle.net/1721.1/121152 2026-04-08T23:01:39Z 2026-04-08T23:01:39Z FastICA Gavert, Hugo Hurri, Jarmo Sarela, Jaakko Hyvarinen, Aapo https://hdl.handle.net/1721.1/124822 2020-04-22T20:25:22Z 1996-01-01T00:00:00Z FastICA Gavert, Hugo; Hurri, Jarmo; Sarela, Jaakko; Hyvarinen, Aapo 1996-01-01T00:00:00Z Parametric Texture Model Portilla, Javier Simoncelli, Eero https://hdl.handle.net/1721.1/124821 2020-04-22T20:20:46Z 2000-01-01T00:00:00Z Parametric Texture Model Portilla, Javier; Simoncelli, Eero See J Portilla and E P Simoncelli. A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients. Int'l Journal of Computer Vision. 40(1):49-71, October, 2000. 2000-01-01T00:00:00Z MATLAB Pyramid Toolbox Simoncelli, Eero https://hdl.handle.net/1721.1/124820 2020-04-22T20:17:05Z 2015-01-01T00:00:00Z MATLAB Pyramid Toolbox Simoncelli, Eero 2015-01-01T00:00:00Z Texture Tiling Model Rosenholtz, Ruth https://hdl.handle.net/1721.1/124819 2020-04-22T20:11:04Z 2020-04-22T00:00:00Z Texture Tiling Model Rosenholtz, Ruth MATLAB software that implements the Texture Tiling Model, a model of human peripheral vision. The model takes as input an image, fixation, and fovea size, and outputs one or more visualizations of the information available in human peripheral vision. 2020-04-22T00:00:00Z Example outputs of the Texture Tiling Model ("mongrels") Rosenholtz, Ruth Yu, Dian Keshvari, Shaiyan https://hdl.handle.net/1721.1/123796 2020-02-12T22:36:22Z 2019-07-01T00:00:00Z Example outputs of the Texture Tiling Model ("mongrels") Rosenholtz, Ruth; Yu, Dian; Keshvari, Shaiyan Our Texture Tiling Model takes a stimulus and fixation as the input, and outputs visualizations of the information available in peripheral vision, according to the model. We call these output images "mongrels". Each input image and fixation corresponds to a number such mongrels. Details that are clear in the mongrels are predicted to be readily available in peripheral vision, and details that are not consistently clear in the mongrels are predicted to be lost or less readily available. This set are additional examples to supplement those shown in our review paper: Ruth Rosenholtz, Dian Yu, Shaiyan Keshvari; Challenges to pooling models of crowding: Implications for visual mechanisms. Journal of Vision 2019;19(7):15. doi: https://doi.org/10.1167/19.7.15. 2019-07-01T00:00:00Z