9,450 research outputs found
Data compression techniques applied to high resolution high frame rate video technology
An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended
Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation
Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86
Spatially coupled inversion of spectro-polarimetric image data I: Method and first results
When inverting solar spectra, image degradation effects that are present in
the data are usually approximated or not considered. We develop a data
reduction method that takes these issues into account and minimizes the
resulting errors. By accounting for the diffraction PSF of the telescope during
the inversions, we can produce a self-consistent solution that best fits the
observed data, while simultaneously requiring fewer free parameters than
conventional approaches. Simulations using realistic MHD data indicate that the
method is stable for all resolutions, including those with pixel scales well
beyond those that can be resolved with a 0.5m telescope, such as the Hinode
SOT. Application of the presented method to reduce full Stokes data from the
Hinode spectro-polarimeter results in dramatically increased image contrast and
an increase in the resolution of the data to the diffraction limit of the
telescope in almost all Stokes and fit parameters. The resulting data allow for
detecting and interpreting solar features that have so far only been observed
with 1m class ground-based telescopes. The new inversion method allows for
accurate fitting of solar spectro-polarimetric imaging data over a large field
of view, while simultaneously improving the noise statistics and spatial
resolution of the results significantly.Comment: A&A, accepte
JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition
This paper proposes a novel algorithm to reassemble an arbitrarily shredded
image to its original status. Existing reassembly pipelines commonly consist of
a local matching stage and a global compositions stage. In the local stage, a
key challenge in fragment reassembly is to reliably compute and identify
correct pairwise matching, for which most existing algorithms use handcrafted
features, and hence, cannot reliably handle complicated puzzles. We build a
deep convolutional neural network to detect the compatibility of a pairwise
stitching, and use it to prune computed pairwise matches. To improve the
network efficiency and accuracy, we transfer the calculation of CNN to the
stitching region and apply a boost training strategy. In the global composition
stage, we modify the commonly adopted greedy edge selection strategies to two
new loop closure based searching algorithms. Extensive experiments show that
our algorithm significantly outperforms existing methods on solving various
puzzles, especially those challenging ones with many fragment pieces
Innovative observing strategy and orbit determination for Low Earth Orbit Space Debris
We present the results of a large scale simulation, reproducing the behavior
of a data center for the build-up and maintenance of a complete catalog of
space debris in the upper part of the low Earth orbits region (LEO). The
purpose is to determine the performances of a network of advanced optical
sensors, through the use of the newest orbit determination algorithms developed
by the Department of Mathematics of Pisa (DM). Such a network has been proposed
to ESA in the Space Situational Awareness (SSA) framework by Carlo Gavazzi
Space SpA (CGS), Istituto Nazionale di Astrofisica (INAF), DM, and Istituto di
Scienza e Tecnologie dell'Informazione (ISTI-CNR). The conclusion is that it is
possible to use a network of optical sensors to build up a catalog containing
more than 98% of the objects with perigee height between 1100 and 2000 km,
which would be observable by a reference radar system selected as comparison.
It is also possible to maintain such a catalog within the accuracy requirements
motivated by collision avoidance, and to detect catastrophic fragmentation
events. However, such results depend upon specific assumptions on the sensor
and on the software technologies
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