Titlebook: Computer-Assisted and Robotic Endoscopy; Third International Terry Peters,Guang-Zhong Yang,Jonathan McLeod Conference proceedings 2017 Spr

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書目名稱Computer-Assisted and Robotic Endoscopy影響因子(影響力)




書目名稱Computer-Assisted and Robotic Endoscopy影響因子(影響力)學(xué)科排名




書目名稱Computer-Assisted and Robotic Endoscopy網(wǎng)絡(luò)公開度




書目名稱Computer-Assisted and Robotic Endoscopy網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer-Assisted and Robotic Endoscopy被引頻次




書目名稱Computer-Assisted and Robotic Endoscopy被引頻次學(xué)科排名




書目名稱Computer-Assisted and Robotic Endoscopy年度引用




書目名稱Computer-Assisted and Robotic Endoscopy年度引用學(xué)科排名




書目名稱Computer-Assisted and Robotic Endoscopy讀者反饋




書目名稱Computer-Assisted and Robotic Endoscopy讀者反饋學(xué)科排名

火海

The European Community and ASEAN,acy according to overlap size. In contrast to the conventional mosaicking approach, the proposed approach can produce panoramic image even in the case of 0% inter-cameras overlap. Additionally, the proposed approach is fast enough for clinical use.

instate

記憶

Toussaint Houeninvo,Philippe Sèdédjiution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

起波瀾

Preoperative Diagnostic Procedures,y deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.

Transfusion

Transfusion

CLASP

鳴叫

ORBSLAM-Based Endoscope Tracking and 3D Reconstruction,ution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

諂媚于性

Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking,y deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.