54,383 research outputs found
A long short-term memory network for vessel reconstruction based on laser doppler flowmetry via a steerable needle
Hemorrhage is one risk of percutaneous intervention in the brain that can be life-threatening. Steerable needles can avoid blood vessels thanks to their ability to follow curvilinear paths, although knowledge of vessel pose is required. To achieve this, we present the deployment of laser Doppler flowmetry (LDF) sensors as an in-situ vessel detection method for steerable needles. Since the perfusion value from an LDF system does not provide positional information directly, we propose the use of a machine learning technique based on a Long Short-term Memory (LSTM) network to perform vessel reconstruction online. Firstly, the LSTM is used to predict the diameter and position of an approaching vessel based on successive measurements of a single LDF probe. Secondly, a "no-go" area is predicted based on the measurement from four LDF probes embedded within a steerable needle, which accounts for the full vessel pose. The network was trained using simulation data and tested on experimental data, with 75 % diameter prediction accuracy and 0.27 mm positional Root Mean Square (RMS) Error for the single probe network, and 77 % vessel volume overlap for the 4-probe setup
Lipreading with Long Short-Term Memory
Lipreading, i.e. speech recognition from visual-only recordings of a
speaker's face, can be achieved with a processing pipeline based solely on
neural networks, yielding significantly better accuracy than conventional
methods. Feed-forward and recurrent neural network layers (namely Long
Short-Term Memory; LSTM) are stacked to form a single structure which is
trained by back-propagating error gradients through all the layers. The
performance of such a stacked network was experimentally evaluated and compared
to a standard Support Vector Machine classifier using conventional computer
vision features (Eigenlips and Histograms of Oriented Gradients). The
evaluation was performed on data from 19 speakers of the publicly available
GRID corpus. With 51 different words to classify, we report a best word
accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural
network-based solution (11.6% improvement over the best feature-based solution
evaluated).Comment: Accepted for publication at ICASSP 201
Long Short-Term Memory Spatial Transformer Network
Spatial transformer network has been used in a layered form in conjunction
with a convolutional network to enable the model to transform data spatially.
In this paper, we propose a combined spatial transformer network (STN) and a
Long Short-Term Memory network (LSTM) to classify digits in sequences formed by
MINST elements. This LSTM-STN model has a top-down attention mechanism profit
from LSTM layer, so that the STN layer can perform short-term independent
elements for the statement in the process of spatial transformation, thus
avoiding the distortion that may be caused when the entire sequence is
spatially transformed. It also avoids the influence of this distortion on the
subsequent classification process using convolutional neural networks and
achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional
Neural Network with STN layer
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