In recent years, digital images and videos of endoscopic screenings and surgical operations are recorded and archived by physicians with increasing frequency. The reason is that interventions can be documented in a much more expressive and informative way than with textual descriptions only. But an extensive, naive usage of endoscopic videos leads to huge amounts of video data that can easily exceed the storage capacity of hospital operators. Moreover, it is a very cumbersome task to retrieve specific information from such a large data set without efficient tools that are tailored to this very specific domain.
Therefore, the purpose of endoViP is to investigate how this video data can be significantly reduced without losing any important information. This involves the development of efficient encoding strategies as well as the automatic identification of irrelevant scenes by means of content-based analysis methods. If irrelevant scenes are omitted and only the actually relevant scenes remain, also the browsing and retrieval performance can be improved considerably. In this context, novel mechanisms for time-efficient retrospective viewing and retrieval of specific relevant scenes will also be investigated.
endoVip is an applied research project conducted at the Institute of Information Technology, Klagenfurt University and funded directly by our project partner KARL STORZ GmbH. The project started on May 1, 2012 and finished on April 30, 2015. It is conducted in close collaboration with the Lakeside Labs Project CodeMM.