I recently started my sabbatical year, where I get a chance to re-tool myself, and my research knowledge. The game plan is to learn how to integrate computer vision (machine vision) and machine learning approaches into my research, in particular with respect to the study of animal behaviour and the analysis of images (and videos). We study the evolutionary genetics of complex phenotypes in my lab. While this used to (mostly) mean the complex structure of the shape and size of fruit-fly wings, we are moving more into the study of animal behavior. In particular how flies evade and escape being eaten by predators (more on that at a later date).
The analysis of such data (both huge sets of wing images as well as video, which is effectively a series of images) can be time consuming and what can be done manually is somewhat limited (such as with JWatcher), in particular if you want to do "high throughput" work with many samples. I have over the past few years interacted, and begun to collaborate with scientists who are using all sorts of techniques from computer vision which have amazed me, both with respect to the speed of the analysis, but also the detailed information gleaned from such approaches. So I am trying to get up to speed and see how to utilize these approaches for my own work.
To that end I will be now posting about this experience (as well as all of the more usual genetics). This will include useful new tidbits, programming scripts, software I have tried (and tutorials), books, and anything else I can think of. Basically my research progress journal for this new endeavour. I hope that this will help me stay nice and organized, and perhaps will be useful more generally. If you start to follow this thread, and have suggestions for anything, please let me know in the comments or on twitter.
More to follow soon!