Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations
To improve the efficiency of surgical trajectory segmentation for 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. Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way. To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data. On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements. Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSimilar Papers 제목 키워드 기반
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation
The video topic segmentation (VTS) task segments videos into intelligible, non-overlapping topics, facilitating efficient comprehension of video content and quick access to specific content. VTS is also critical to vario…
Contrastive LearningMixture-of-ExpertsScene SegmentationSegmentation+1Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction
The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inferenc…
Bayesian OptimizationPredictionTrajectory PredictionOne-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Maps
Robotic manipulation tasks often require simultaneous reasoning over motion and contact forces, yet most Learning from Demonstration (LfD) methods model only spatial trajectories and neglect force interactions with the e…
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak dif…
Machine TranslationTranslationUnsupervised Machine TranslationLearning Motion and Temporal Cues for Unsupervised Video Object Segmentation
In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. Unlike previous methods t…
Objectobject-detectionObject DetectionSalient Object Detection+6