kesshijordan Goto Github PK

afq

Automated Fiber Quantification

autoslp

brainbrowser-minimal-example

Minimal BrainBrowser example, BrainBrowser -- https://brainbrowser.cbrain.mcgill.ca

cluster-viz

Browser-based visualization tool for cluster-based quality-control of tractography output to facilitate modeling of white matter fascicles

dipy

Diffusion MR Imaging in Python

dipy_qa

Experimental large scale quality assurance framework for DIPY

dipy_workshop

Materials for teaching the Dipy workshop for the 2018 OHBM Hackathon.

kesh_autoseg_tools

This is a set of tools associated with automatically segmenting tractography datasets using a combination of two complementary approaches: streamlines included in a fascicle model are identified based on 1) anatomical connectivity priors based on Freesurfer-derived regions of interest (ROIs) in the subject’s native space and 2) shape priors based on 3D streamline bundle atlases applied using Recobundles. The code for a methodology generating bundle atlases is also included, as well as useful command-line helper scripts such as identification of outliers, filtering, and merging functions.

kesshi_sandbox

Sandbox repo for trying things

kesshijordan.github.io

medulina.github.io

Brain segmentation game

messyversionclusterviz

nhw_17_testing

nhw_testing_example

nibabel

Python package to access a cacophony of neuro-imaging file formats

nipype

Workflows and interfaces for neuroimaging packages

publication_repository

sharing methods and interesting science

pyafq

Automated Fiber Quantification ... in Python

scikit-learn

scikit-learn: machine learning in Python

shared_notebooks

shared_scripts

sharing miscellaneous scripts

synthetic-video-generation-for-autonomous-cars

A huge challenge for autonomous vehicles(ACs) is to have a dataset that captures real-world multitudinous driving conditions. The currently available video datasets are not annotated & most of them aren't high resolution videos which is again an impediment for object detection. I am excited to solve this problem by annotating & generating photo-realistic synthetic video dataset for ACs using DeepLab, conditional GANs.

touchscreen_ml

for our machine learning class