17,558 research outputs found
Greedy Search for Descriptive Spatial Face Features
Facial expression recognition methods use a combination of geometric and
appearance-based features. Spatial features are derived from displacements of
facial landmarks, and carry geometric information. These features are either
selected based on prior knowledge, or dimension-reduced from a large pool. In
this study, we produce a large number of potential spatial features using two
combinations of facial landmarks. Among these, we search for a descriptive
subset of features using sequential forward selection. The chosen feature
subset is used to classify facial expressions in the extended Cohn-Kanade
dataset (CK+), and delivered 88.7% recognition accuracy without using any
appearance-based features.Comment: International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 201
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
In this paper we evaluate performance of data-dependent hashing methods on
binary data. The goal is to find a hashing method that can effectively produce
lower dimensional binary representation of 512-bit FREAK descriptors. A
representative sample of recent unsupervised, semi-supervised and supervised
hashing methods was experimentally evaluated on large datasets of labelled
binary FREAK feature descriptors
Group-level Emotion Recognition using Transfer Learning from Face Identification
In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand
Challenge
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
This study proposed a deep learning-based tracking method for ultrasound (US)
image-guided radiation therapy. The proposed cascade deep learning model is
composed of an attention network, a mask region-based convolutional neural
network (mask R-CNN), and a long short-term memory (LSTM) network. The
attention network learns a mapping from a US image to a suspected area of
landmark motion in order to reduce the search region. The mask R-CNN then
produces multiple region-of-interest (ROI) proposals in the reduced region and
identifies the proposed landmark via three network heads: bounding box
regression, proposal classification, and landmark segmentation. The LSTM
network models the temporal relationship among the successive image frames for
bounding box regression and proposal classification. To consolidate the final
proposal, a selection method is designed according to the similarities between
sequential frames. The proposed method was tested on the liver US tracking
datasets used in the Medical Image Computing and Computer Assisted
Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by
three experienced observers to obtain their mean positions. Five-fold
cross-validation on the 24 given US sequences with ground truths shows that the
mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all
landmarks are within 2 mm. We further tested the proposed model on 69 landmarks
from the testing dataset that has a similar image pattern to the training
pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental
results have demonstrated the feasibility and accuracy of our proposed method
in tracking liver anatomic landmarks using US images, providing a potential
solution for real-time liver tracking for active motion management during
radiation therapy
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