9,752 research outputs found
Data-driven synthesis of composite-feature detectors for 3D image analysis
Most image analysis techniques are based upon low level descriptions of the data. It is important that the chosen representation is able to discriminate as much as possible among independent image features. In particular, this is of great importance in segmentation with deformable models, which must be guided to the target object boundary avoiding other image features. In this paper, we present a multiresolution method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined band-pass energy filters according to their ability to segregate the different features in the image. In this way, the method generates a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is accomplished by defining a distance metric between the frequency features that reflects the degree of alignment of their energy maxima. This distance is related to the mutual information of their responses' energy maps. As will be shown, the method is able to isolate the frequency components of independent visual patterns in 3D images. We have applied this composite-feature detection method to the initialization of active models. Among the visual patterns detected, those associated to the segmentation target are selected by user interaction to define the initial state of a geodesic active model. We will demonstrate that this initialization technique facilitates the evolution of the model to the proper boundary.S
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Needs, trends, and advances in scintillators for radiographic imaging and tomography
Scintillators are important materials for radiographic imaging and tomography
(RadIT), when ionizing radiations are used to reveal internal structures of
materials. Since its invention by R\"ontgen, RadIT now come in many modalities
such as absorption-based X-ray radiography, phase contrast X-ray imaging,
coherent X-ray diffractive imaging, high-energy X- and ray radiography
at above 1 MeV, X-ray computed tomography (CT), proton imaging and tomography
(IT), neutron IT, positron emission tomography (PET), high-energy electron
radiography, muon tomography, etc. Spatial, temporal resolution, sensitivity,
and radiation hardness, among others, are common metrics for RadIT performance,
which are enabled by, in addition to scintillators, advances in high-luminosity
accelerators and high-power lasers, photodetectors especially CMOS pixelated
sensor arrays, and lately data science. Medical imaging, nondestructive
testing, nuclear safety and safeguards are traditional RadIT applications.
Examples of growing or emerging applications include space, additive
manufacturing, machine vision, and virtual reality or `metaverse'. Scintillator
metrics such as light yield and decay time are correlated to RadIT metrics.
More than 160 kinds of scintillators and applications are presented during the
SCINT22 conference. New trends include inorganic and organic scintillator
heterostructures, liquid phase synthesis of perovskites and m-thick films,
use of multiphysics models and data science to guide scintillator development,
structural innovations such as photonic crystals, nanoscintillators enhanced by
the Purcell effect, novel scintillator fibers, and multilayer configurations.
Opportunities exist through optimization of RadIT with reduced radiation dose,
data-driven measurements, photon/particle counting and tracking methods
supplementing time-integrated measurements, and multimodal RadIT.Comment: 45 pages, 43 Figures, SCINT22 conference overvie
A review of advances in pixel detectors for experiments with high rate and radiation
The Large Hadron Collider (LHC) experiments ATLAS and CMS have established
hybrid pixel detectors as the instrument of choice for particle tracking and
vertexing in high rate and radiation environments, as they operate close to the
LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for
which the tracking detectors will be completely replaced, new generations of
pixel detectors are being devised. They have to address enormous challenges in
terms of data throughput and radiation levels, ionizing and non-ionizing, that
harm the sensing and readout parts of pixel detectors alike. Advances in
microelectronics and microprocessing technologies now enable large scale
detector designs with unprecedented performance in measurement precision (space
and time), radiation hard sensors and readout chips, hybridization techniques,
lightweight supports, and fully monolithic approaches to meet these challenges.
This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog.
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