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.
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
REJECT 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation
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.