There are 2 repositories under human-vision topic.
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.
[TIP-2018] MATLAB implementation of the "A Gabor Feature-Based Quality Assessment Model for the Screen Content Images"
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).
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
Code used to create the Neural Encoding Datasets (NED), and utility functions to generate neural responses for arbitrary images using NED's trained models.
Test framework and reference implementation of our algorithms relating to the real-time simulation of human vision.
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.