135,361 research outputs found
Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)
Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in
image-guided radiation therapy to provide volumetric images with high spatial
and temporal resolutions to enable applications including tumor motion
tracking/prediction and intra-delivery dose calculation/accumulation. However,
the dynamic CBCT reconstruction is a substantially challenging spatiotemporal
inverse problem, due to the extremely limited projection sample available for
each CBCT reconstruction (one projection for one CBCT volume). Approach: We
developed a simultaneous spatial and temporal implicit neural representation
(STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image
and the evolution of its motion into spatial and temporal multi-layer
perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs
via acquired projections to represent the dynamic CBCT series. In addition to
the MLPs, we also introduced prior knowledge, in form of principal component
analysis (PCA)-based patient-specific motion models, to reduce the complexity
of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction
problem. We used the extended cardiac torso (XCAT) phantom to simulate
different lung motion/anatomy scenarios to evaluate STINR. The scenarios
contain motion variations including motion baseline shifts, motion
amplitude/frequency variations, and motion non-periodicity. The scenarios also
contain inter-scan anatomical variations including tumor shrinkage and tumor
position change. Main results: STINR shows consistently higher image
reconstruction and motion tracking accuracy than a traditional PCA-based method
and a polynomial-fitting based neural representation method. STINR tracks the
lung tumor to an averaged center-of-mass error of <2 mm, with corresponding
relative errors of reconstructed dynamic CBCTs <10%
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
In this work we present a novel end-to-end framework for tracking and
classifying a robot's surroundings in complex, dynamic and only partially
observable real-world environments. The approach deploys a recurrent neural
network to filter an input stream of raw laser measurements in order to
directly infer object locations, along with their identity in both visible and
occluded areas. To achieve this we first train the network using unsupervised
Deep Tracking, a recently proposed theoretical framework for end-to-end space
occupancy prediction. We show that by learning to track on a large amount of
unsupervised data, the network creates a rich internal representation of its
environment which we in turn exploit through the principle of inductive
transfer of knowledge to perform the task of it's semantic classification. As a
result, we show that only a small amount of labelled data suffices to steer the
network towards mastering this additional task. Furthermore we propose a novel
recurrent neural network architecture specifically tailored to tracking and
semantic classification in real-world robotics applications. We demonstrate the
tracking and classification performance of the method on real-world data
collected at a busy road junction. Our evaluation shows that the proposed
end-to-end framework compares favourably to a state-of-the-art, model-free
tracking solution and that it outperforms a conventional one-shot training
scheme for semantic classification
Video-based Sign Language Recognition without Temporal Segmentation
Millions of hearing impaired people around the world routinely use some
variants of sign languages to communicate, thus the automatic translation of a
sign language is meaningful and important. Currently, there are two
sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that
recognizes word by word and continuous SLR that translates entire sentences.
Existing continuous SLR methods typically utilize isolated SLRs as building
blocks, with an extra layer of preprocessing (temporal segmentation) and
another layer of post-processing (sentence synthesis). Unfortunately, temporal
segmentation itself is non-trivial and inevitably propagates errors into
subsequent steps. Worse still, isolated SLR methods typically require strenuous
labeling of each word separately in a sentence, severely limiting the amount of
attainable training data. To address these challenges, we propose a novel
continuous sign recognition framework, the Hierarchical Attention Network with
Latent Space (LS-HAN), which eliminates the preprocessing of temporal
segmentation. The proposed LS-HAN consists of three components: a two-stream
Convolutional Neural Network (CNN) for video feature representation generation,
a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention
Network (HAN) for latent space based recognition. Experiments are carried out
on two large scale datasets. Experimental results demonstrate the effectiveness
of the proposed framework.Comment: 32nd AAAI Conference on Artificial Intelligence (AAAI-18), Feb. 2-7,
2018, New Orleans, Louisiana, US
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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