C3 - Self-supervised Visual Place Recognition for Colonoscopy Sequences
Javier Morlana, Pablo Azagra Millán, Javier Civera, José M. M. Montiel
We present the first place recognition system trained specifically for colonoscopy sequences. We use the convolutional neural network for image retrieval proposed by Radenovic et al. and we fine-tune it using image pairs from real human colonoscopies. The colonoscopy frames are clustered automatically by a Structure-from-Motion (SfM) algorithm, which has proven to cope with scene deformation and illumination changes. The experiments show that the system is able to generalize by testing in a different human colonoscopy, retrieving frames observing the same place despite of the different viewpoint and illumination changes. The proposed place recognition would be a key component of Simultaneous Localization and Mapping (SLAM) systems operating in colonoscopy to assist doctors during the explorations or to support robotization.
Wednesday 7th July
C1-9 (short): Endoscopy and Validation Studies - 16:45 - 17:30 (UTC+2)