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ipsych's Projects

harvesters icon harvesters

🌈 Friendly Image Acquisition Library for Computer Vision People

hcp2bids icon hcp2bids

To convert Human Connectome projects(HCP) data to BIDS std

head2head icon head2head

PyTorch implementation for Head2Head and Head2Head++. It can be used to fully transfer the head pose, facial expression and eye movements from a source video to a target identity.

imglab icon imglab

To speedup and simplify image labeling/ annotation process with multiple supported formats.

interfacegan icon interfacegan

CVPR'20 paper `Interpreting the Latent Space of GANs for Semantic Face Editing`

intsy icon intsy

32/64 channel bioamplifier system

key_stroke_lsl icon key_stroke_lsl

Tiny matlab script to register a keystroke in the keyboard and send a marker to the LabStreamingLayer (LSL) Lab Recorder.

lasp icon lasp

Low-latency Audio Signal Processing plugin for Unity

livianet icon livianet

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

locationsimulator icon locationsimulator

MacOS application to spoof / fake / mock your iOS / iPadOS or iPhoneSimulator device location. WatchOS and TvOS are partially supported.

lstm_rnn_tutorials_with_demo icon lstm_rnn_tutorials_with_demo

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

med2image icon med2image

Converts medical images to more displayable formats, e.g. NIfTI to jpg.

mfsda icon mfsda

Multivariate Functional Shape Data Analysis (MFSDA) is a Matlab based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.

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