527 research outputs found
Mining the ESO WFI and INT WFC archives for known Near Earth Asteroids. Mega-Precovery software
The ESO/MPG WFI and the INT WFC wide field archives comprising 330,000 images
were mined to search for serendipitous encounters of known Near Earth Asteroids
(NEAs) and Potentially Hazardous Asteroids (PHAs). A total of 152 asteroids (44
PHAs and 108 other NEAs) were identified using the PRECOVERY software, their
astrometry being measured on 761 images and sent to the Minor Planet Centre.
Both recoveries and precoveries were reported, including prolonged orbital arcs
for 18 precovered objects and 10 recoveries. We analyze all new opposition data
by comparing the orbits fitted before and after including our contributions. We
conclude the paper presenting Mega-Precovery, a new online service focused on
data mining of many instrument archives simultaneously for one or a few given
asteroids. A total of 28 instrument archives have been made available for
mining using this tool, adding together about 2.5 million images forming the
Mega-Archive.Comment: Accepted for publication in Astronomische Nachrichten (Sep 2012
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
We propose a new iterative segmentation model which can be accurately learned
from a small dataset. A common approach is to train a model to directly segment
an image, requiring a large collection of manually annotated images to capture
the anatomical variability in a cohort. In contrast, we develop a segmentation
model that recursively evolves a segmentation in several steps, and implement
it as a recurrent neural network. We learn model parameters by optimizing the
interme- diate steps of the evolution in addition to the final segmentation. To
this end, we train our segmentation propagation model by presenting incom-
plete and/or inaccurate input segmentations paired with a recommended next
step. Our work aims to alleviate challenges in segmenting heart structures from
cardiac MRI for patients with congenital heart disease (CHD), which encompasses
a range of morphological deformations and topological changes. We demonstrate
the advantages of this approach on a dataset of 20 images from CHD patients,
learning a model that accurately segments individual heart chambers and great
vessels. Com- pared to direct segmentation, the iterative method yields more
accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop,
MICCAI 201
An explorative study of interface support for image searching
In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which - according to our hypothesis - helps the user to onceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and di®erent types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall
Lumen Border Detection of Intravascular Ultrasound via Denoising of Directional Wavelet Representations
In this paper, intravascular ultrasound (IVUS) grayscale images, acquired with a single-element mechanically rotating transducer, are processed with wavelet denoising and region-based segmentation to extract various layers of lumen contours and plaques. First, IVUS volumetric data is expanded on complex exponential wavelet-like basis functions, also known as Brushlets, which are well localized in time and frequency domains. Brushlets denoising have demonstrated in the past a great aptitude for denoising ultrasound data and removal of blood speckles. A region-based segmentation framework is then applied for detection of lumen border layers, which remains one of the most challenging problems in IVUS image analysis for images acquired with a single element, mechanically rotating 45 MHz transducer. We evaluated hard thresholding for Brushlet denoising, and compared segmentation results to manually traced lumen borders. We observed good agreement and suggest that the proposed algorithm has a great potential to be used as a reliable pre-processing step for accurate lumen border detection
Using Open Source Libraries in the Development of Control Systems Based on Machine Vision
The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented. © 2020, IFIP International Federation for Information Processing.Foundation for Assistance to Small Innovative Enterprises in Science and Technology, FASIEFunding. The work was performed under state contract 3170ΓC1/48564, grant from the FASIE
Nemo: a computational tool for analyzing nematode locomotion
The nematode Caenorhabditis elegans responds to an impressive range of
chemical, mechanical and thermal stimuli and is extensively used to investigate
the molecular mechanisms that mediate chemosensation, mechanotransduction and
thermosensation. The main behavioral output of these responses is manifested as
alterations in animal locomotion. Monitoring and examination of such
alterations requires tools to capture and quantify features of nematode
movement. In this paper, we introduce Nemo (nematode movement), a
computationally efficient and robust two-dimensional object tracking algorithm
for automated detection and analysis of C. elegans locomotion. This algorithm
enables precise measurement and feature extraction of nematode movement
components. In addition, we develop a Graphical User Interface designed to
facilitate processing and interpretation of movement data. While, in this
study, we focus on the simple sinusoidal locomotion of C. elegans, our approach
can be readily adapted to handle complicated locomotory behaviour patterns by
including additional movement characteristics and parameters subject to
quantification. Our software tool offers the capacity to extract, analyze and
measure nematode locomotion features by processing simple video files. By
allowing precise and quantitative assessment of behavioral traits, this tool
will assist the genetic dissection and elucidation of the molecular mechanisms
underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8
The GALFA-HI Compact Cloud Catalog
We present a catalog of 1964 isolated, compact neutral hydrogen clouds from
the Galactic Arecibo L-Band Feed Array Survey Data Release One (GALFA-HI DR1).
The clouds were identified by a custom machine-vision algorithm utilizing
Difference of Gaussian kernels to search for clouds smaller than 20'. The
clouds have velocities typically between |VLSR| = 20-400 km/s, linewidths of
2.5-35 km/s, and column densities ranging from 1 - 35 x 10^18 cm^-2. The
distances to the clouds in this catalog may cover several orders of magnitude,
so the masses may range from less than a Solar mass for clouds within the
Galactic disc, to greater than 10^4 Solar Masses for HVCs at the tip of the
Magellanic Stream. To search for trends, we separate the catalog into five
populations based on position, velocity, and linewidth: high velocity clouds
(HVCs); galaxy candidates; cold low velocity clouds (LVCs); warm, low
positive-velocity clouds in the third Galactic Quadrant; and the remaining warm
LVCs. The observed HVCs are found to be associated with previously-identified
HVC complexes. We do not observe a large population of isolated clouds at high
velocities as some models predict. We see evidence for distinct histories at
low velocities in detecting populations of clouds corotating with the Galactic
disc and a set of clouds that is not corotating.Comment: 34 Pages, 9 Figures, published in ApJ (2012, ApJ, 758, 44), this
version has the corrected fluxes and corresponding flux histogram and masse
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