WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- L.A.M.A. AI using Reinforcement Learning
- Intrusion Detection system
- Competitive coding
- Why The Hype Around GANs
- 3D reconstruction using 2D images
- Computer Vision Workbench
- 3D Object Classification using Mesh Neural Network
- Lossless high entropy compression algorithm
- ML GYM
- Tools for Web Development
- Strategy Wars [Online]
- Food Recommendation through Machine Learning
- Conversational Chatbot
- Virtual Keyboard
- Super Shenron
- Gestures for 3D space
- Road Network 3D Rendering using OpenGL
- Face Recognition using Statistics
- Introduction to Kaggle and Machine Learning
- Krittika Website
- Rubik's cube solver
- Planet/Atmosphere Renderer using OpenGL
- Digital Depth Perception
- KontaKt App
- Tinkerers’ Laboratory Website
- Graphic Intensive MUSIC APP
- Pool It!
- Insti Buddy
- Intelligent agents
Producing 2D images of a 3D world is inherently a lossy process, i.e. the entire geometric richness of 3D gets projected onto a single flat 2D image. We aim to create an API in Python which primarily reconstructs 3D volumes from 2D X-Ray Images.
We see this project as the first step towards a diagnostic tool in conditions where either no CT equipment or the education to interpret x-ray imagery is available, such as for mobile x-ray devices, lay users, or medical diagnostics in developing countries. The project is primarily divided into 2 parts:
Implementation of various CNN architectures for 3D reconstruction from 2D images(3 people would be working on this part)
Development of API(back-end framework for the above task).1 mentee would be working on this part.
Part 1 has some hard pre-requisites while anyone who has an interest in python or has done some basic programming in python or java-script can apply for part 2.
Pre-requisites for part 1: Must be familiar with any one of the following deep learning frameworks: Pytorch/Tensorflow/Theano/Keras. A basic idea of neural networks and machine learning is required. Previous experience in image processing is desired although is not a hard pre-requisite.
Interested people in this part should go through the following paper while applying:
Note: If you are new to deep learning, it is recommended that you should go through the first 5 chapters of the book before applying:
|Week 1 and 2(Pre-Endsems)||Reading of related material and learning relevant applications of the framework that would be used(mostly Keras and PyTorch)|
|Week 3||Testing and implementing Simple CNN architectures|
|Week 4 and 5||Working on Designing and implementation of 3D reconstruction from multiple images along with data pre-processing|
|Week 6 and 7||Programming and testing of various models for 3D reconstruction from single 2D image|
|Week 8||Further improvements on the models that have been created above.|