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• W2893473994 abstract "To improve the efficiency of surgical trajectory segmentation for surgical assessment and robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. An unsupervised deep learning network called dense convolutional encoder-decoder network (DCED-Net) is first proposed to extract more discriminative features from videos in an effective way. DCED-Net has several advantages. It compresses the encoding-decoding structure, strengthens the feature propagation, and avoids the manual annotation. To further improve the accuracy of segmentation, on one hand, a modified transition state clustering model is employed with a strategy of reducing the redundancy of transition points. On the other hand, the segmentation results are promoted by identifying the over-segmented trajectories based on predefined similarity measurements. Extensive experiments on the public data set JIGSAWS show that with our method, the percentage increase in accuracy is 20.3% and the percentage decrease in time cost is 92.6%." @default.
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• W2893473994 date "2018-01-01" @default.
• W2893473994 modified "2023-10-14" @default.
• W2893473994 title "A Fast Unsupervised Approach for Multi-Modality Surgical Trajectory Segmentation" @default.
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• W2893473994 doi "https://doi.org/10.1109/access.2018.2872635" @default.
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