Speeding up Scientific Imaging Workflows- Design of Automated Image Annotation Tool
Wed 16 January 2013 by Dr. Dirk ColbryBlog post edited by Anonymous - "Migrated to Confluence 4.0"
Below are the slides that I presented at the User Centered Computer Vision workshop.
Abstract:
Low cost digital cameras have transformed the process of collecting data.
Researchers can now generate massive datasets and analyze the images later,
either manually or with the assistance of software tools for processing and
annotating images. However, it remains time-consuming and expensive to develop
custom software analysis tools for a specific research problem or domain – and
often these custom tools cannot scale to larger datasets or adapt to new
research questions. Existing image analysis tools also work best with well-
defined research projects, where the researchers know what information to
extract from each image. Yet, for new projects, it is especially difficult to
build useful software, where researchers have not yet determined what
information is “interesting” within the images. One way to increase the
efficiency of the research is to improve the workflow of this exploration
process. This paper presents one approach for improving exploratory image
analysis workflows using point-based image annotation. We describe a landmark
labeling system that (1) assists researchers in identifying interesting
features and annotating images, (2) evolves over time to automate the
annotation process, and (3) can be readily scaled and adapted to explore new
problems and new domains. We describe both a proof-of-concept system in
current use, and ongoing work to develop a generalizable software tool to
support fully automated image annotation, with the ultimate goal of allowing
researchers to explore data faster and significantly reduces the mean time to
science.
Blogpost migrated from ICER Wiki using custom python script. Comment on errors below.