Coherent Laplacian 3-D protrusion segmentation

Cuzzolin, Fabio; Mateusy, D.; Knossow, David; Boyer, Edmond; Horaud, Radu

doi:10.1109/cvpr.2008.4587452

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Another interesting work is Cuzzolin et al's method [11] that computes protrusion segmentation on point cloud sequences. This method is based on the detection of shape extremities, such as hands or legs.…”

Section: Segmentation Of Temporally Incoherent Mesh Sequencesmentioning

confidence: 99%

Segmentation of temporal mesh sequences into rigidly moving components

et al. 2013

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International audienceIn this paper is considered the segmentation of meshes into rigid components given temporal sequences of deforming meshes. We propose a fully automatic approach that identifies model parts that consistently move rigidly over time. This approach can handle meshes independently reconstructed at each time instant. It allows therefore for sequences of meshes with varying connectivities as well as varying topology. It incrementally adapts, merges and splits segments along a sequence based on the coherence of motion information within each segment. In order to provide tools for the evaluation of the approach, we also introduce new criteria to quantify a mesh segmentation. Results on both synthetic and real data as well as comparisons are provided in the paper

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Section: Segmentation Of Temporally Incoherent Mesh Sequencesmentioning

confidence: 99%

Segmentation of temporal mesh sequences into rigidly moving components

et al. 2013

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“…The intermediate layers, whose size is smaller than both the input and the output layers, are designed for learning compact semantics as well as reducing noise. In fact, dimensionality reduction has been shown in the past to improve the result of other forms of unsupervised learning such as clustering [12]. Following this rationale, Jefferson et al have proposed an anomaly detection system for videos which uses autoencoders and predictive convolutional LSTMs.…”

Section: B Rationale For Unsupervised Deep Anomaly Detectionmentioning

confidence: 99%

Unsupervised Anomaly Detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS) Using a Deep Residual Autoencoder

Samuel

Cuzzolin

2021

IEEE Robot. Autom. Lett.

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Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires a human expert monitoring the procedure from a console, whereas automated anomaly detection systems in this area typically rely on classical supervised learning. Anomalous surgical events, however, are rare, making it difficult to capture data to train a model in a supervised fashion. In this work we propose an unsupervised approach to anomaly detection for robotic MIS based on deep residual autoencoders. The idea is to make the autoencoder learn the 'normal' distribution of the data and detect abnormal events deviating from this distribution by measuring a reconstruction error. The model is trained and validated upon both the publicly available Cholec80 dataset and a set of videos captured on procedures using artificial anatomies ('phantoms') as part of the Smart Autonomous Robotic Assistant Surgeon (SARAS) project. The system achieves recall and precision equal to 78.4%, 91.5%, respectively, on Cholec80 and of 95.6%, 88.1% on the SARAS phantom dataset. The system was developed and deployed as part of the SARAS platform for real-time anomaly detection with a processing time of 25 ms per frame.

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“…Diffusion based methods alleviate the problem of pose variance [dGGV08, SOCG10, CMK*, GBAL09]. In [Rus07] the authors define GPS coordinates on the mesh which are clustered using a k ‐means algorithm to segment a shape.…”

Section: Related Workmentioning

confidence: 99%