GridFix Introduction ==================== GridFix is a Python toolbox to facilitate preprocessing of scene fixation data for region-based analysis using Generalized Linear Mixed Models (GLMM). Our `recently published manuscript `_ [1] describes how this approach can be used to evaluate models of visual saliency above and beyond content-independent biases. Please also see [2] for a previous description of the approach and `our ECVP 2016 poster `_ for an overview about the structure and workflow of the GridFix toolbox. Example data and Jupyter Notebooks for the "Tutorial" section are also available for download at https://github.com/ischtz/gridfix-tutorial/releases. .. image:: _static/example_grid.png Features -------- - Define image parcellations (region masks) based on a regular grid (RegionSet), with other parcellation types planned for future versions - Apply these parcellations to collections of images or saliency maps (ImageSet) - Define features to assign a value to each region X image (e.g., mean saliency of each region) - Explicitly model central viewer bias using different approaches (e.g. euclidean distance, Gaussian) - Output the resulting feature vectors for GLMM-based analysis (see [1-2]) - Create R source code for subsequent GLMM analysis using lme4 Citing GridFix -------------- If you use GridFix, please cite the following publication: Nuthmann, A., Einhäuser, W., & Schütz, I. (2017). How well can saliency models predict fixation selection in scenes beyond central bias? A new approach to model evaluation using generalized linear mixed models. Frontiers in Human Neuroscience. http://doi.org/10.3389/fnhum.2017.00491 It is also possible to cite the software itself. In this case, please use the following Zenodo DOI: .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.998554.svg :target: https://doi.org/10.5281/zenodo.998554 References ---------- [1] Nuthmann, A., Einhäuser, W., & Schütz, I. (2017). How well can saliency models predict fixation selection in scenes beyond central bias? A new approach to model evaluation using generalized linear mixed models. Frontiers in Human Neuroscience. http://doi.org/10.3389/fnhum.2017.00491 [2] Nuthmann, A., & Einhäuser, W. (2015). A new approach to modeling the influence of image features on fixation selection in scenes. Annals of the New York Academy of Sciences, 1339(1), 82-96. http://dx.doi.org/10.1111/nyas.12705