Rennert, Peter;
(2019)
User Assisted Computer Vision for Biology.
Doctoral thesis (Ph.D), UCL (University College London).
Abstract
Computer vision is a broad field that combines aspects of image and signal processing with machine learning to solve tasks that involve analysis of image and video data. Biology is a science that increasingly produces such data on a scale that requires automation to analyse it. Challenges in biology include the high variability of the data, the unpredictability of the data and the novelty of computer vision and machine learning to the field. In this thesis we identify the lack of labelling tools which support exploration, annotation and visualisation of scientific recordings over a long time-span as the main bottleneck for computer vision being used in biology. As a solution to that problem, we present two new tools, AudioTagger and VideoTagger, which were used in large scale studies. We present the results of a 3 month life-span behavioural assay which was conducted using VideoTagger. We apply computer vision to count D. melanogaster eggs and introduce an interface which supports iterative learning and annotation to train the classifier. We show promising results for user assisted computer vision on the example of a user guided motion prediction system.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | User Assisted Computer Vision for Biology |
| Event: | UCL (University College London) |
| Language: | English |
| UCL classification: | UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069228 |
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