Update May 12, 2020: I intend to start the course during the week of May 18, 2020. If you’re interested in taking it, please do the following by May 15, 2020: (i) Get the up-to-date syllabus here; (ii) Write your name and email address on this sheet if you haven’t already contacted me; (iii) Note the Drive folder with images here; and (iv) get started with Module 1! I’ll plan on running a weekly meeting — time TBA, but see the sheet — to provide some structure, introductions, and opportunities to ask questions, though as noted, you’re largely on your own. (Of course, that means you don’t actually have to contact me at all — the materials will be accessible. You may find it useful to get emails from me, though.)
Motivated in part by the large number of graduate students and postdocs currently working on improving their programming skills while being stuck at home because of the Covid-19 pandemic, I’m posting the outline of an “informal image analysis course” I’ve taught in the past, and that I’m offering to teach again through weekly video chats. In this post I’ve pasted the beginning of the document describing the course; the rest of the description is here (PDF).
It’s not quite a stand-alone course, since I make use of readings from Digital Image Processing by Gonzalez and Woods and other sources, but I can figure out how to share or bypass these.
Background and Motivations
In 2014, 2015, and 2016 I taught an informal (“off the books”) course on computational image analysis. The goal was to help people (1) learn about methods for extracting information from images, and (2) improve their skills in writing code to implement these methods. The things we image, the ways we image them, and the assessments we want to make based on images are all extremely varied, and the ability to develop custom software tailored to specific needs is invaluable. In addition, familiarity with common image processing methods is useful for planning and discussing experiments, in part to better assess what tasks are easy or difficult. Rather remarkably, there are no formal courses on image analysis offered at the University of Oregon as far as I know, providing additional motivation for this course. The subjects we cover also intersect important topics in statistics and data analysis, such as maximum likelihood estimation, that are not typically taught to biology or physics students.
The audience for the course was graduate students, postdocs, and advanced undergraduates in physics and biology. As it was an informal course (taught outside my usual teaching load), and the students all had considerable intrinsic motivation, we met just once a week and did not have any assessments such as quizzes or exams. During the weekly meetings we discussed problems with the previous week’s topic and assignments, shared results, asked questions, and introduced the next module. The sessions were quite enjoyable. Approximately 10 people participated each time – the number would likely be higher if I more actively advertised the course.
This document describes the topics, readings, and assignments for the course. Several sections need improvement, both so that this will be a more useful stand-alone resource, and so that I can improve the course next time!
Click here for the rest of the course description, which includes the list of topics. [Updated May 12, 2020]
I quickly painted two antibiotic capsules.
— Raghuveer Parthasarathy, May 5, 2020