25,708 research outputs found
On a shape adaptive image ray transform
A conventional approach to image analysis is to perform separately feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points using the Hough transform. The original image Ray Transform (IRT) demonstrated capability to extract structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges, the new capability is achieved by addition of a single parameter that controls which shape is elected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. We confirm the new capability by application of conventional methods for exact shape location. We analyze performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes. Further research could capitalize on the new extraction ability to extend descriptive capability
Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing
We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionaly, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Joint signal extraction from galaxy clusters in X-ray and SZ surveys: A matched-filter approach
The hot ionized gas of the intra-cluster medium emits thermal radiation in
the X-ray band and also distorts the cosmic microwave radiation through the
Sunyaev-Zel'dovich (SZ) effect. Combining these two complementary sources of
information through innovative techniques can therefore potentially improve the
cluster detection rate when compared to using only one of the probes. Our aim
is to build such a joint X-ray-SZ analysis tool, which will allow us to detect
fainter or more distant clusters while maintaining high catalogue purity. We
present a method based on matched multifrequency filters (MMF) for extracting
cluster catalogues from SZ and X-ray surveys. We first designed an X-ray
matched-filter method, analogous to the classical MMF developed for SZ
observations. Then, we built our joint X-ray-SZ algorithm by combining our
X-ray matched filter with the classical SZ-MMF, for which we used the physical
relation between SZ and X-ray observations. We show that the proposed X-ray
matched filter provides correct photometry results, and that the joint matched
filter also provides correct photometry when the relation
of the clusters is known. Moreover, the proposed joint algorithm provides a
better signal-to-noise ratio than single-map extractions, which improves the
detection rate even if we do not exactly know the relation.
The proposed methods were tested using data from the ROSAT all-sky survey and
from the Planck survey.Comment: 22 pages (before appendices), 19 figures, 3 tables, 5 appendices.
Accepted for publication in A&
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