Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)

Code to create Stylized-ImageNet, a stylized version of standard ImageNet (ICLR 2019 Oral)

Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)

Use DNNs to build encoding models of EEG visual responses.

Data and materials from the paper "Comparing deep neural networks against humans: object recognition when the signal gets weaker" (arXiv 2017)

Python package to conduct feature-reweighted representational similarity analysis.

Load and model the brain data of the Algonauts Project 2023 Challenge.

Functions to generate neural responses for arbitrary images using trained encoding models.

Repository related to the manuscript "Automatic Gemstone Classification Using Computer Vision" by Bona Hiu Yan Chow and Constantino Carlos Reyes-Aldasoro published in Minerals MDPI, 2022, 12(1), 60 (doi: 10.3390/min12010060).

[TIP-2018] MATLAB implementation of the "A Gabor Feature-Based Quality Assessment Model for the Screen Content Images"

Matlab implementation of "Image quality assessment using human visual DOG model fused with random forest"

Software for Environmental Light Field (ELF) analysis. For more information, check out our Interface article "Quantifying biologically essential aspects of environmental light" at https://doi.org/10.1098/rsif.2021.0184

Visual Search Model: A Bayesian model for visual search on natural scenes.

Test framework and reference implementation of our algorithms relating to the real-time simulation of human vision.

Deep fMRI Encoding Models of Human Vision using Capsule Networks

A course project for Android application built for an image classifier

A test to see how many shades of gray can a human distinguish, written with HTML/CSS/JavaScript.

Neckerworld is a computer game designed to teach and explore human and computer vision.

This is a curated collection exploring animal vision and perception, compared to human capabilities.