990 research outputs found
Spatiotemporal Barcodes for Image Sequence Analysis
Taking as input a time-varying sequence of two-dimensional
(2D) binary images, we develop an algorithm for computing a spatiotemporal
0âbarcode encoding lifetime of connected components on the image
sequence over time. This information may not coincide with the one provided
by the 0âbarcode encoding the 0âpersistent homology, since the
latter does not respect the principle that it is not possible to move backwards
in time. A cell complex K is computed from the given sequence,
being the cells of K classified as spatial or temporal depending on whether
they connect two consecutive frames or not. A spatiotemporal path is
defined as a sequence of edges of K forming a path such that two edges
of the path cannot connect the same two consecutive frames. In our
algorithm, for each vertex v â K, a spatiotemporal path from v to the
âoldestâ spatiotemporally-connected vertex is computed and the corresponding
spatiotemporal 0âbar is added to the spatiotemporal 0âbarcode.Junta de AndalucĂa FQM-369Ministerio de EconomĂa y Competitividad MTM2012-3270
Image sequence analysis and merging
Peer ReviewedPostprint (published version
Online parameter estimation in dynamic Markov Random Fields for image sequence analysis
pre-printMarkov Random Fields (MRF) have proven to be extremely useful models for efficient and accurate image segmentation.Recent literature points to an increased effort towards incorporating useful priors (shape, geometry, context) in a MRF framework. However, topological priors, considered extremely crucial in biological and natural image sequences have been less explored. This work proposes a strategy wherein free parameters of the MRF are used to make it topology aware using a semantic graphical model working in conjunction with the MRF. Estimation of free parameters is constrained by prior knowledge of an object's topological dynamics encoded by the graphical model. Maximizing a regional conformance measure yields parameters for the frame under consideration. The application motivating this work is the tracing of neuronal structures across 3D serial section Transmission Electron Micrograph (ssTEM) stacks. Applicability of the proposed method is demonstrated by tracing 3D structures in ssTEM stacks
Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning
Reflection high-energy electron diffraction (RHEED) is a powerful tool in
molecular beam epitaxy (MBE), but RHEED images are often difficult to
interpret, requiring experienced operators. We present an approach for
automated surveillance of GaAs substrate deoxidation in MBE using deep learning
based RHEED image-sequence classification. Our approach consists of an
non-supervised auto-encoder (AE) for feature extraction, combined with a
supervised convolutional classifier network. We demonstrate that our
lightweight network model can accurately identify the exact deoxidation moment.
Furthermore we show that the approach is very robust and allows accurate
deoxidation detection during months without requiring re-training. The main
advantage of the approach is that it can be applied to raw RHEED images without
requiring further information such as the rotation angle, temperature, etc.Comment: 6 pages, 6 figure
Biomedical image sequence analysis with application to automatic quantitative assessment of facial paralysis
Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance
Image sequence analysis for emerging interactive multimedia services - The European COST 211 framework
Cataloged from PDF version of article.Flexibility and efficiency of coding, content extraction,
and content-based search are key research topics in
the field of interactive multimedia. Ongoing ISO MPEG-4 and
MPEG-7 activities are targeting standardization to facilitate such
services. European COST Telecommunications activities provide
a framework for research collaboration. COST 211bis and COST
211ter activities have been instrumental in the definition and
development of the ITU-T H.261 and H.263 standards for videoconferencing
over ISDN and videophony over regular phone
lines, respectively. The group has also contributed significantly
to the ISO MPEG-4 activities. At present a significant effort
of the COST 211ter group activities is dedicated toward image
and video sequence analysis and segmentationâan important
technological aspect for the success of emerging object-based
MPEG-4 and MPEG-7 multimedia applications. The current
work of COST 211 is centered around the test model, called
the Analysis Model (AM). The essential feature of the AM is
its ability to fuse information from different sources to achieve
a high-quality object segmentation. The current information
sources are the intermediate results from frame-based (still) color
segmentation, motion vector based segmentation, and changedetection-based
segmentation. Motion vectors, which form the
basis for the motion vector based intermediate segmentation, are
estimated from consecutive frames. A recursive shortest spanning
tree (RSST) algorithm is used to obtain intermediate color and
motion vector based segmentation results. A rule-based region
processor fuses the intermediate results; a postprocessor further
refines the final segmentation output. The results of the current
AM are satisfactory; it is expected that there will be further
improvements of the AM within the COST 211 project
Biomedical image sequence analysis with application to automatic quantitative assessment of facial paralysis
Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance
Analysis of optical flow models in the framework of calculus of variations
In image sequence analysis, variational optical flow computations require the solution of a parameter dependent optimization problem with a data term and a regularizer. In this paper we study existence and uniqueness of the optimizers. Our studies rely on quasiconvex functionals on the spaces WÂč,P(Ω, IRd), with p > 1, BV(Ω, IRd), BD(&Omeag;). The methods that are covered by our results include several existing techniques. Experiments are presented that illustrate the behavior of these approaches
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