The presentation for the Zurich Java User Group on 13 Jan 2016
- The slides can be seen here
- A Demo of our Quantitative Image Search Engine
We are delighted to invite you to our official founding apéro on the 22nd of January in Colab Zurich. We'd love to give you a little background on the sort of things we've been tinkering away at in our apartments and garages and show you why we think that 2016 is the year of image analysis. As listening to any of us talk for more than a few minutes would get a bit dry, we're going to spice things up with a few interactive demos to see how algorithms can improve your outfits, find your long lost celebrity pairing, and even combat terrorists. We'll also be demonstrating a few of our more standard projects looking for cancer, detecting malaria, and counting cars. And if even that doesn't excite you, we'll have plenty of snacks, drinks, and good company.
Try and let us know by this Friday the 15th (info@4quant.com), so we can plan a bit better. If not, spontaneous guests are always welcome. Feel free to bring friends and partners along.
Details
Date: 22 January 2016 Time: from 5PM on Location: Colab Zurich / Auer & Co., Sihlquai 131, 8005 Zürich (Entrance on the backside of the building)
- Quantitative Big Imaging Course
- X-Ray Imaging Group at ETH Zurich
- Presentation at Spark Summit 2014
- Spark Introduction
Kevin Mader is the founder of 4Quant and a lecturer in the X-ray Microscopy Group within the Department for Information Technology and Electrical Engineering at ETH Zurich. His research focuses on turning big hairy 3D images into simple, robust, reproducible numbers without resorting to black boxes or magic. In particular, as part of several collaborations, he is currently working on automatically segmenting full animal zebrafish images, characterizing rheology in 3D flows, and measuring viral infection dynamics in cell lines.
As acquisition speeds and stability improve, new possibilities have been opened in imaging large samples at high resolution. Of particular interest have been massive scale projects like the Human Brain Project, adult Zebra fish imaging, and personalized or precision medicine. All involve thousands to millions of measurements at the highest possible resolutions to cover mm to m length scales. The task of processing and analyzing such large collections of measurements is exceptionally difficult. In this work, we address the processing terabytes worth of measurements in a parallel, distributed manner. Building on the distributed frameworks of Apache Spark and Spark Imaging Layer, we have extended the common tomographic and image processing tools to work on these images enabling the use of many machines in parallel and drastically accelerating the speed and ease with which these large datasets can be stitched and analyzed. Our most recent developments enable the data to be analyzed and processed in real-time using the latest Big Data streaming techniques allowing for fault-tolerant, distributed analytics to process complicated datasets and eventually provide feedback to both experimentalists and their equipment to allow for adaptation of measurements.