Open AccessSimultaneous shape and camera‐projector parameter estimation for 3D endoscopic system using CNN‐based grid‐oneshot scan
Author(s) -
Furukawa Ryo,
Nagamatsu Genki,
Oka Shiro,
Kotachi Takahiro,
Okamoto Yuki,
Tanaka Shinji,
Kawasaki Hiroshi
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2019.0070
Subject(s) - computer science , artificial intelligence , bundle adjustment , projector , computer vision , convolutional neural network , feature (linguistics) , projection (relational algebra) , grid , feature extraction , iterative reconstruction , pattern recognition (psychology) , image (mathematics) , algorithm , mathematics , philosophy , linguistics , geometry
For effective in situ endoscopic diagnosis and treatment, measurement of polyp sizes is important. For this purpose, 3D endoscopic systems have been researched. Among such systems, an active stereo technique, which projects a special pattern wherein each feature is coded, is a promising approach because of simplicity and high precision. However, previous works of this approach have problems. First, the quality of 3D reconstruction depended on the stabilities of feature extraction from the images captured by the endoscope camera. Second, due to the limited pattern projection area, the reconstructed region was relatively small. In this Letter, the authors propose a learning‐based technique using convolutional neural networks to solve the first problem and an extended bundle adjustment technique, which integrates multiple shapes into a consistent single shape, to address the second. The effectiveness of the proposed techniques compared to previous techniques was evaluated experimentally.