ImageXD

Image processing across domains


ImageXD 2017

Day 1, March 29th

Learn

Please install the required software and datasets prior to the tutorials.

8:30 - 9:00 : Coffee

9:00–10:00 : NumPy, SciPy, and Matplotlib, an introduction to numerical analysis with Python. This tutorial is aimed at those familiar with Python, but who have not used NumPy, SciPy, and Matplotlib extensively before.

10:00–11:30 : scikit-image, image processing in Python. We introduce the scikit-image library, demonstrate its various capabilities, and do several hands-on exercises to practice its use.

11:30–12:00 : Dask for parallel processing.

12:00–1:00 : Lunch (on your own)

1:00–2:30 : ImageJ, image processing in Java. We will explore ImageJ and its SciJava interface with examples in Python and Clojure.

2:30–4:00 : Vani Mandava from Microsoft Research will present image processing use-cases of the Azure cloud.

4:00–5:00 : TensorFlow and Keras. Deep neural networks in Python. In this tutorial we learn how to train a neural network using the TensorFlow library, and how to then apply it to scientific data.

Day 2, March 30th

Discuss

Notes

Schedule:

8:30 - 9:00 : Coffee

9:00 : Welcome

9:15–9:45 : Stella Yu, University of California, Berkeley: Learning to See the Image, Seeing to Learn the World

9:45–10:15 : Talita Perciano, Lawrence Berkeley National Lab.: Image Segmentation Across Domains using Parallel Markov Random Field Technique. Video.

10:30–12:00 : Breakout sessions

12:00–1:00 : Lunch break

1:00–1:30 : Mario Juric, The University of Washington: Object Detection in Astronomy: Current Practice & Challenges Going Forward

1:30–2:00 : Ira Kemelmacher-Shlizerman, The University of Washington: Modeling people from historical photos. Video.

2:00–2:30 : Claire McQuin, Broad Institute: Developing High Content Image Analysis Software for Biologists. Video.

2:30–3:00 : Lydia Ng, Allen Institute for Brain Science: Delivering data, tools and knowledge on brain cell types, connections and activity. Video.

3:15–3:45 : Kyle Harrington, University of Idaho: Biological Image Analysis using the ImageJ/Fiji Software Ecosystem

4:00–4:45 : Breakout sessions

4:45 Wrap-up for the day

5:00 : Reception

Day 3, March 31st

Create

We will get together and work in teams to develop ideas around data, software, algorithms, and learning. This open format will allow participants to learn from each other, work together, and explore concepts that cross domain boundaries.

8:30 - 9:00 : Coffee

9:00 - 10:00 : Project ideas discussion

10:00 - 12:00 : Group work

12:00 - 1:00 : Lunch (on your own)

1:00 - 1:30 : Updates

1:30 - 4:30 : Group work

4:30 - 5:00 : Final presentations

For visitors from out of town:

http://www.universityinnseattle.com/

http://www.watertownseattle.com/


Blog


About

Incredible advances are being made in image processing techniques and tools, but the scientists who use them typically don’t have the opportunity to communicate with scientists who work on similar problems in different domains.

ImageXD comprises researchers from a variety of fields who work with images as a primary source of data. We work to identify common principles, algorithms, and tools. We aim learn from one another while strengthening ties across disciplinary boundaries.


subscribe via RSS