1,618 research outputs found
Assessment of Left Ventricular Function in Cardiac MSCT Imaging by a 4D Hierarchical Surface-Volume Matching Process
Multislice computed tomography (MSCT) scanners offer new
perspectives for cardiac kinetics evaluation with 4D dynamic
sequences of high contrast and spatiotemporal resolutions. A new
method is proposed for cardiac motion extraction in multislice CT.
Based on a 4D hierarchical surface-volume matching process, it
provides the detection of the heart left cavities along the
acquired sequence and the estimation of their 3D surface velocity
fields. A Markov random field model is defined to find, according
to topological descriptors, the best correspondences between a 3D
mesh describing the left endocardium at one time and the 3D
acquired volume at the following time. The global optimization of
the correspondences is realized with a multiresolution process.
Results obtained on simulated and real data show the capabilities
to extract clinically relevant global and local motion parameters
and highlight new perspectives in cardiac computed tomography
imaging
Video Transformers: A Survey
Transformer models have shown great success handling long-range interactions,
making them a promising tool for modeling video. However they lack inductive
biases and scale quadratically with input length. These limitations are further
exacerbated when dealing with the high dimensionality introduced with the
temporal dimension. While there are surveys analyzing the advances of
Transformers for vision, none focus on an in-depth analysis of video-specific
designs. In this survey we analyze main contributions and trends of works
leveraging Transformers to model video. Specifically, we delve into how videos
are handled as input-level first. Then, we study the architectural changes made
to deal with video more efficiently, reduce redundancy, re-introduce useful
inductive biases, and capture long-term temporal dynamics. In addition we
provide an overview of different training regimes and explore effective
self-supervised learning strategies for video. Finally, we conduct a
performance comparison on the most common benchmark for Video Transformers
(i.e., action classification), finding them to outperform 3D ConvNets even with
less computational complexity
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
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