yanhuanhuanyy Goto Github PK

cm2013_sleep_staging

Project to predict sleep stages based on EEG, ECG, EOG data. Signals are filtered using different methods (Wavelet, Butterworth). The prediction was done using feature extraction in combination with SVM, Shallow NN and CNN.

cross-domain-detection

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].

deepsleepnet

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

dthb3d_reg

This package performs 3D non-rigid image registration for medical and synthetic images using truncated hierarchical B-splines (THB-Splines)

eeg-datasets

A list of all public EEG-datasets

flownet2-tf

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

hello-world2

image-registration

Mutual information + gradient information

image-registration-1

A MATLAB library/toolbox providing access to image registration suitable for use with medical images.

imageregistration

Medical Image Registration using Mutual Information

mitoolbox

Mutual Information functions for C and MATLAB

mutual-information

基于互信息的图像配准软件

psnr_ssim

Peak signal-to-noise ratio and structural similarity index can calculate image quality

pytorch-cyclegan-and-pix2pix

Image-to-Image Translation in PyTorch

registrationproject

University 4th year engineering project. Implements medical non-rigid image registration.

simple-sgm

Implementation of a simplified version of the SemiGlobal Matching algorithm (Hirschmuller, H. (2005). Accurate and Efficient Stereo Processing by Semi Global Matching and Mutual Information. CVPR .)

superpoint

Efficient neural feature detector and descriptor

superpoint_logfile

rpautrat/SuperPoint training log file

superpointpretrainednetwork

PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)

tensorflow-zh

谷歌全新开源人工智能系统TensorFlow官方文档中文版

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习