講演名 | 2014-03-14 Study of Recognizing Hand Actions from Video Sequences during Suture Surgeries Based on Temporally-Sectioned SIFT and Sliding Window Based Neural Networks , |
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抄録(英) | Towards the realization of a robotic nurse that can support surgeries autonomously by recognizing surgical situations only using video informations, this paper proposes an improved method by using sectioned-SIFT and sliding window based neural network that can recognize surgeon's hand actions: suture and tying. Hand area is detected by using color information and then the video sequence is partitioned into sections. Sectioned-SIFT descriptors are computed in each section and built a word vocabulary. Histogram feature of the action is spliced by using word's frequency in each section. Finally, sliding window and neural network is used to recognize the significant actions: suture and tying. The proposed method has achieved the 100% recognition rate for manually extracted actions and 90% recognition rate for whole surgery video sequences. |
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キーワード(英) | action recognition / sectioned-SIFT / BP neural network / RSN |
資料番号 | PRMU2013-193 |
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研究会 | PRMU |
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開催期間 | 2014/3/6(から1日開催) |
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申込み研究会 | Pattern Recognition and Media Understanding (PRMU) |
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本文の言語 | ENG |
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タイトル(英) | Study of Recognizing Hand Actions from Video Sequences during Suture Surgeries Based on Temporally-Sectioned SIFT and Sliding Window Based Neural Networks |
サブタイトル(和) | |
キーワード(1)(和/英) | / action recognition |
第 1 著者 氏名(和/英) | / Ye Li |
第 1 著者 所属(和/英) | Graduate School of Global Information and Telecommunication Studies, Waseda University |
発表年月日 | 2014-03-14 |
資料番号 | PRMU2013-193 |
巻番号(vol) | vol.113 |
号番号(no) | 493 |
ページ範囲 | pp.- |
ページ数 | 6 |
発行日 |