Hands-on MATLAB Workshop

Practical Applications of Deep Learning in Image Processing and Computer Vision

Tuesday, October 9, 12:10 - 14:10, Kokkali Room

Deep learning has rapidly evolved over the past decade and is now being used in fields varying from autonomous systems to medical image processing. This tutorial will cover practical applications of deep learning in image processing and computer vision. In this interactive hands-on workshop you will access a MATLAB-session through a browser to write code to:

  1. Learn the fundamentals of deep learning and understand terms like “layers”, “networks”, and “loss”
  2. Graphically build a deep neural networks
  3. Access models from TensorFlow, Caffe and PyTorch in MATLAB
  4. Automate labeling of ground truth for semantic segmentation, object detection and image classification
  5. Apply deep learning for image processing tasks including image denoising and super-resolution
  6. Create a deep learning model for semantic segmentation and object detection

Session details and registrations can be found at www.mathworks.com/ICIP2018Workshop.

This is an interactive hands-on session, instructors will provide access to MATLAB along with training data and scripts via a browser. Please carry your own laptop for the session. Contact Avinash Nehemiah for more information.

Presenter Info:

Avinash Nehemiah, Product Manager, MathWorks

Avinash Nehemiah, product marketing manager for computer vision, automated driving and deep learning at MathWorks, has ten years of experience in computer vision. Prior to joining MathWorks he led a team that created a computer vision-based solution for patient safety in hospital rooms. Avinash has a Master’s degree in electrical and computer engineering from Carnegie Mellon University, where his research focused on object recognition in radar imagery.

Jeff Mather, Image Processing Toolbox - Development Manager and Senior Software Engineer, MathWorks

Jeff Mather is a senior software engineer and the development manager of the Image Processing Toolbox. He has managed the team since 2013 and has developed features for the toolbox and MATLAB since 2000, particularly in the area of file formats, medical image processing, HDR imaging, color science, and software performance optimization. He has an undergraduate degree in mathematics from Grinnell College and a Master of Software Engineering from Brandeis University. He has been with MathWorks since 1998.