At the 26th International Conference on MultiMedia Modeling (MMM 2020) in Daejeon, Korea, researchers from ITEC have successfully presented several scientific contributions to the multimedia community. First, Natalia Sokolova presented her first paper on “Evaluating the Generalization Performance of Instrument Classification in Cataract Surgery Videos”. Next, Sabrina Kletz presented her work on “Instrument Recognition in Laparoscopy for Technical Skill Assessment”. Finally, Andreas Leibetseder talked about “GLENDA: Gynecologic Laparoscopy Endometriosis Dataset”.

The paper “Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network” has been accepted for publication at the “IEEE International Symposium on Biomedical Imaging”, located at Iowa City, Iowa, USA (April 3-7, 2020). This conference is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS).
Authors: Negin Ghamsarian, Klaus Schoeffmann, Mario Taschwer

Abstract: A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement.

Keywords: Video Deblurring, Deconvolutional Neural Networks, Cataract Surgery Videos

Acknowledgment: This work was funded by the FWF Austrian Science Fund under grant P 31486-N31

Natalia Sokolova

VBS 2020 in Daejeon (South Korea) was an amazing event with a lot of fun! Eleven teams, each consisting of two users (coming from 11 different countries) competed against each other in both a private session for about 5 hours and a public session for almost 3 hours. ITEC did also participate with two teams. In total all teams had to solve 22 challenging video retrieval tasks, issued on a shared dataset consisting of 1000 hours of content (V3C1)! Many thanks go to the VBS teams but also to the VBS organizers as well as the local organizers, who did a great job and made VBS2020 a wonderful and entertaining event!