21,980 research outputs found
Multi-Scale Spatially Weighted Local Histograms in O(1)
Weighting pixel contribution considering its location is a key feature in
many fundamental image processing tasks including filtering, object modeling
and distance matching. Several techniques have been proposed that incorporate
Spatial information to increase the accuracy and boost the performance of
detection, tracking and recognition systems at the cost of speed. But, it is
still not clear how to efficiently ex- tract weighted local histograms in
constant time using integral histogram. This paper presents a novel algorithm
to compute accurately multi-scale Spatially weighted local histograms in
constant time using Weighted Integral Histogram (SWIH) for fast search. We
applied our spatially weighted integral histogram approach for fast tracking
and obtained more accurate and robust target localization result in comparison
with using plain histogram.Comment: 5 pages, 7 figure
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Locally Orderless Registration
Image registration is an important tool for medical image analysis and is
used to bring images into the same reference frame by warping the coordinate
field of one image, such that some similarity measure is minimized. We study
similarity in image registration in the context of Locally Orderless Images
(LOI), which is the natural way to study density estimates and reveals the 3
fundamental scales: the measurement scale, the intensity scale, and the
integration scale.
This paper has three main contributions: Firstly, we rephrase a large set of
popular similarity measures into a common framework, which we refer to as
Locally Orderless Registration, and which makes full use of the features of
local histograms. Secondly, we extend the theoretical understanding of the
local histograms. Thirdly, we use our framework to compare two state-of-the-art
intensity density estimators for image registration: The Parzen Window (PW) and
the Generalized Partial Volume (GPV), and we demonstrate their differences on a
popular similarity measure, Normalized Mutual Information (NMI).
We conclude, that complicated similarity measures such as NMI may be
evaluated almost as fast as simple measures such as Sum of Squared Distances
(SSD) regardless of the choice of PW and GPV. Also, GPV is an asymmetric
measure, and PW is our preferred choice.Comment: submitte
Dynamic Phase Transition, Universality, and Finite-size Scaling in the Two-dimensional Kinetic Ising Model in an Oscillating Field
We study the two-dimensional kinetic Ising model below its equilibrium
critical temperature, subject to a square-wave oscillating external field. We
focus on the multi-droplet regime where the metastable phase decays through
nucleation and growth of many droplets of the stable phase. At a critical
frequency, the system undergoes a genuine non-equilibrium phase transition, in
which the symmetry-broken phase corresponds to an asymmetric stationary limit
cycle for the time-dependent magnetization. We investigate the universal
aspects of this dynamic phase transition at various temperatures and field
amplitudes via large-scale Monte Carlo simulations, employing finite-size
scaling techniques adopted from equilibrium critical phenomena. The critical
exponents, the fixed-point value of the fourth-order cumulant, and the critical
order-parameter distribution all are consistent with the universality class of
the two-dimensional equilibrium Ising model. We also study the cross-over from
the multi-droplet to the strong-field regime, where the transition disappears
Simulations of Clusters of Galaxies
The degree of complexity and, to a somewhat lesser degree, realism in
simulations has advanced rapidly in the past few years. The simplest approach -
modeling a cluster as collisionless dark matter and collisonal, non--radiative
gas is now fairly well established. One of the most fruitful results of this
approach is the {\sl morphology--cosmology connection} for X-ray clusters.
Simulations have provided the means to make concrete predictions for the X-ray
morphologies of clusters in cosmologies with different , with the
result that low cosmologies fair rather poorly when compared to
observations. Another result concerns the accuracy of \xray binding mass
estimates. The standard, hydrostatic, isothermal model estimator is found to be
accurate to typically better than at radii where the density contrast is
between and . More complicated approaches, which attempt to
explicitly follow galaxy formation within the proto--cluster environment are
slowly being realized. The key issue of {\sl dynamical biasing} of the galaxy
population within a cluster is being probed, but conclusive answers are
lacking. The dynamics of multi--phase gas, including conversion of cold, dense
gas into stars and the feedback therefrom, is the largest obstacle hindering
progress. An example demonstrating the state--of--the--art in this area is
presented.Comment: to appear in Proceedings of the XIVth Moriond Astrophysics Meeting.
10 pages, uuencoded, compressed postscript file includes figures (~1 Mb after
unpacked
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery.Comment: PhD Dissertation (162 pages
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