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2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security
Anomalousness Detection for Surgery Videos Using CHLAC Feature
Edingburgh, United Kingdom
August 20-August 21
ISBN: 978-0-7695-3754-2
| ASCII Text | x | ||
| Fumio Sakabe, Masahiro Murakawa, Takumi Kobayashi, Tetsuya Higuchi, Nobuyuki Otsu, "Anomalousness Detection for Surgery Videos Using CHLAC Feature," 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, pp. 66-68, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/BLISS.2009.13, author = {Fumio Sakabe and Masahiro Murakawa and Takumi Kobayashi and Tetsuya Higuchi and Nobuyuki Otsu}, title = {Anomalousness Detection for Surgery Videos Using CHLAC Feature}, journal ={2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security}, volume = {0}, year = {2009}, isbn = {978-0-7695-3754-2}, pages = {66-68}, doi = {http://doi.ieeecomputersociety.org/10.1109/BLISS.2009.13}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security TI - Anomalousness Detection for Surgery Videos Using CHLAC Feature SN - 978-0-7695-3754-2 SP66 EP68 A1 - Fumio Sakabe, A1 - Masahiro Murakawa, A1 - Takumi Kobayashi, A1 - Tetsuya Higuchi, A1 - Nobuyuki Otsu, PY - 2009 KW - surgery video KW - anomalousness detection KW - CHLAC KW - chapter mark KW - surgical safety support system VL - 0 JA - 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BLISS.2009.13
We propose a chapter mark addition method for surgery video application that adopts cubic higher-order local auto-correlation (CHLAC) feature. In our method normal motions, which frequently occur in front of video cameras, are learnt statistically with CHLAC in combination with the subspace method. An anomalous motion is detected as a motion that exists far from the learnt subspace for the motions frequently-observed, and a chapter mark is placed just before the position the anomalous motion is recorded on the video. We conducted preliminary experiments using surgery video data to confirm effectiveness of the method we propose. The results show that the proposed method can detect the motions not frequently-observed in a surgery operation.
Index Terms:
surgery video, anomalousness detection, CHLAC, chapter mark, surgical safety support system
Citation:
Fumio Sakabe, Masahiro Murakawa, Takumi Kobayashi, Tetsuya Higuchi, Nobuyuki Otsu, "Anomalousness Detection for Surgery Videos Using CHLAC Feature," bliss, pp.66-68, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, 2009
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