Neonate segmentation project at NIRAL, UNC, with Dr. Martin Styner.
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Updated
Apr 12, 2019 - Jupyter Notebook
Neonate segmentation project at NIRAL, UNC, with Dr. Martin Styner.
semantic-segmentation
using deep learning (semantic segmentation, FCN) to find drivable parts of the road
Labeled the pixels of a road in images using a Fully Convolutional Network (FCN).
This Project is Semantic Segmentation Project of Term 3 of Udacity Self-Driving Car Engineer Nanodegree.
A real-time application of the LIGHT-SERNET model
Master's thesis
Protein Residue Contact Prediction based on a Deep Neural Architecture
Pixel segmentation of roads from dashboard camera using Fully Convolutional Network
A robot motion planning simulator that can efficiently navigate partially observable environments using deep learning
An API that detect expiration date from the product package's picture based on Deep Learning Algorithms
Multi-Planar UNet for autonomous segmentation of 3D medical images
Fully convolutional deep neural network to remove transparent overlays from images
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