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M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification

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arxiv 1610.05541 v2 pith:RKVWFW4V submitted 2016-10-18 cs.CV

Rémi Cadène , Thomas Robert , Nicolas Thome , Matthieu Cord This is my paper · ORCID
classification cs.CV
keywords smoothingtemporalchallengeclassificationframeshiddenm2caimarkov
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Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this technical report, we compare several classification models and temporal smoothing methods. Our submitted solution is a fine tuned Residual Network-200 on 80% of the training set with temporal smoothing using simple temporal averaging of the predictions and a Hidden Markov Model modeling the sequence.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation

    cs.CV 2025-01 reject novelty 5.0 of 10

    Kinematics Adaptive Frame Recognition selects frames with significant tool motion, reducing training data 5x to 10x and modestly improving phase segmentation accuracy over uniform sampling.

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