721 research outputs found
On the segmentation and classification of hand radiographs
This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components
Subspace Representations and Learning for Visual Recognition
Pervasive and affordable sensor and storage technology enables the acquisition of an ever-rising amount of visual data. The ability to extract semantic information by interpreting, indexing and searching visual data is impacting domains such as surveillance, robotics, intelligence, human- computer interaction, navigation, healthcare, and several others. This further stimulates the investigation of automated extraction techniques that are more efficient, and robust against the many sources of noise affecting the already complex visual data, which is carrying the semantic information of interest. We address the problem by designing novel visual data representations, based on learning data subspace decompositions that are invariant against noise, while being informative for the task at hand. We use this guiding principle to tackle several visual recognition problems, including detection and recognition of human interactions from surveillance video, face recognition in unconstrained environments, and domain generalization for object recognition.;By interpreting visual data with a simple additive noise model, we consider the subspaces spanned by the model portion (model subspace) and the noise portion (variation subspace). We observe that decomposing the variation subspace against the model subspace gives rise to the so-called parity subspace. Decomposing the model subspace against the variation subspace instead gives rise to what we name invariant subspace. We extend the use of kernel techniques for the parity subspace. This enables modeling the highly non-linear temporal trajectories describing human behavior, and performing detection and recognition of human interactions. In addition, we introduce supervised low-rank matrix decomposition techniques for learning the invariant subspace for two other tasks. We learn invariant representations for face recognition from grossly corrupted images, and we learn object recognition classifiers that are invariant to the so-called domain bias.;Extensive experiments using the benchmark datasets publicly available for each of the three tasks, show that learning representations based on subspace decompositions invariant to the sources of noise lead to results comparable or better than the state-of-the-art
The Diversity of Diffuse Ly Nebulae around Star-Forming Galaxies at High Redshift
We report the detection of diffuse Ly emission, or Ly halos
(LAHs), around star-forming galaxies at and in the NOAO
Deep Wide-Field Survey Bo\"otes field. Our samples consist of a total of
1400 galaxies, within two separate regions containing spectroscopically
confirmed galaxy overdensities. They provide a unique opportunity to
investigate how the LAH characteristics vary with host galaxy large-scale
environment and physical properties. We stack Ly images of different
samples defined by these properties and measure their median LAH sizes by
decomposing the stacked Ly radial profile into a compact galaxy-like
and an extended halo-like component. We find that the exponential scale-length
of LAHs depends on UV continuum and Ly luminosities, but not on
Ly equivalent widths or galaxy overdensity parameters. The full
samples, which are dominated by low UV-continuum luminosity Ly emitters
(), exhibit LAH sizes of 5kpc. However, the
most UV- or Ly-luminous galaxies have more extended halos with
scale-lengths of 7kpc. The stacked Ly radial profiles decline
more steeply than recent theoretical predictions that include the contributions
from gravitational cooling of infalling gas and from low-level star formation
in satellites. On the other hand, the LAH extent matches what one would expect
for photons produced in the galaxy and then resonantly scattered by gas in an
outflowing envelope. The observed trends of LAH sizes with host galaxy
properties suggest that the physical conditions of the circumgalactic medium
(covering fraction, HI column density, and outflow velocity) change with halo
mass and/or star-formation rates.Comment: published in ApJ, minor proof corrections applie
Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection
We propose a new model-free segmentation method, JULES, which combines recent
statistical multiresolution techniques with local deconvolution for
idealization of ion channel recordings. The multiresolution criterion takes
into account scales down to the sampling rate enabling the detection of
flickering events, i.e., events on small temporal scales, even below the filter
frequency. For such small scales the deconvolution step allows for a precise
determination of dwell times and, in particular, of amplitude levels, a task
which is not possible with common thresholding methods. This is confirmed
theoretically and in a comprehensive simulation study. In addition, JULES can
be applied as a preprocessing method for a refined hidden Markov analysis. Our
new methodolodgy allows us to show that gramicidin A flickering events have the
same amplitude as the slow gating events. JULES is available as an R function
jules in the package clampSeg
Detecting and tracking people in real-time
The problem of detecting and tracking people in images and video has been the subject of a great deal of research, but remains a challenging task. Being able to detect and track people would have an impact in a number of fields, such as driverless vehicles, automated surveillance, and human-computer interaction. The difficulties that must be overcome include coping with variations in appearance between different people, changes in lighting, and the ability to detect people across multiple scales. As well as having high accuracy, it is desirable for a technique to evaluate an image with low latency between receiving the image and producing a result.
This thesis explores methods for detecting and tracking people in images and video. Techniques are implemented on a desktop computer, with an emphasis on low latency. The problem of detection is examined first. The well established integral channel features detector is introduced and reimplemented, and various novelties are implemented in regards to the features used by the detector. Results are given to quantify the accuracy and the speed of the developed detectors on the INRIA person dataset. The method is further extended by examining the prospect of using multiple classifiers in conjunction. It is shown that using a classifier with a version of the same classifier reflected in the vertical axis can improve performance. A novel method for clustering images of people to find modes of appearance is also presented. This involves using boosting classifiers to map a set of images to vectors, to which K-means clustering is applied. Boosting classifiers are then trained on these clustered datasets to create sets of multiple classifiers, and it is demonstrated that these sets of classifiers can be evaluated on images with only a small increase in the running time over single classifiers.
The problem of single target tracking is addressed using the mean shift algorithm. Mean shift tracking works by finding the best colour match for a target from frame to frame. A novel form of mean shift tracking through scale is developed, and the problem of multiple target tracking is addressed by using boosting classifiers in conjunction with Kalman filters. Tests are carried out on the CAVIAR dataset, which gives representative examples of surveillance scenarios, to show the performance of the proposed approaches.Open Acces
Multiloop functional renormalization group approach to quantum spin systems
Renormalization group methods are well-established tools for the (numerical)
investigation of the low-energy properties of correlated quantum many-body
systems, allowing to capture their scale-dependent nature. The functional
renormalization group (FRG) allows to continuously evolve a microscopic model
action to an effective low-energy action as a function of decreasing energy
scales via an exact functional flow equation, which is then approximated by
some truncation scheme to facilitate computation. Here, we report on our
transcription of a recently developed multiloop truncation approach for
electronic FRG calculations to the pseudo-fermion functional renormalization
group (pf-FRG) for interacting quantum spin systems. We discuss in detail the
conceptual intricacies of the flow equations generated by the multiloop
truncation, as well as essential refinements to the integration scheme for the
resulting integro-differential equations. To benchmark our approach we analyze
antiferromagnetic Heisenberg models on the pyrochlore, simple cubic and
face-centered cubic lattice, discussing the convergence of physical observables
for higher-loop calculations and comparing with existing results where
available. Combined, these methodological refinements systematically improve
the pf-FRG approach to one of the numerical tools of choice when exploring
frustrated quantum magnetism in higher spatial dimensions.Comment: 22 pages, 9 figure
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
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