TY - CHAP
T1 - A Visual Model Approach for Parsing Colonoscopy Videos
AU - Cao, Yu
AU - Tavanapong, Wallapak
AU - Li, Dalei
AU - Oh, Junghwan
AU - De Groen, Piet C.
AU - Wong, Johnny
PY - 2004
Y1 - 2004
N2 - Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable as an educational resource for endoscopic research, a platform to assess procedural skills for endoscopists, and a platform for mining for unknown abnormality patterns that may lead to colorectal cancer. The first necessary step for the analysis is parsing for semantic units. In this paper, we propose a new visual model approach that employs visual features extracted directly from compressed videos together with audio analysis to discover important semantic units called scenes. Our experimental results show average precision and recall of 93% and 85%, respectively.
AB - Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable as an educational resource for endoscopic research, a platform to assess procedural skills for endoscopists, and a platform for mining for unknown abnormality patterns that may lead to colorectal cancer. The first necessary step for the analysis is parsing for semantic units. In this paper, we propose a new visual model approach that employs visual features extracted directly from compressed videos together with audio analysis to discover important semantic units called scenes. Our experimental results show average precision and recall of 93% and 85%, respectively.
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U2 - 10.1007/978-3-540-27814-6_22
DO - 10.1007/978-3-540-27814-6_22
M3 - Chapter
AN - SCOPUS:35048844831
SN - 3540225390
SN - 9783540225393
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 160
EP - 169
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Enser, Peter
A2 - Kompatsiaris, Yiannis
A2 - O’Connor, Noel E.
A2 - Smeaton, Alan F.
A2 - Smeulders, Arnold W. M.
PB - Springer Verlag
ER -