1,989 research outputs found
Efficient Techniques for Management and Delivery of Video Data
The rapid advances in electronic imaging, storage, data compression telecommunications, and networking technology have resulted in a vast creation and use of digital videos in many important applications such as digital libraries, distance learning, public information systems, electronic commerce, movie on demand, etc. This brings about the need for management as well as delivery of video data. Organizing and managing video data, however, is much more complex than managing conventional text data due to their semantically rich and unstructured contents. Also, the enormous size of video files requires high communication bandwidth for data delivery. In this dissertation, I present the following techniques for video data management and delivery. Decomposing video into meaningful pieces (i.e., shots) is a very fundamental step to handling the complicated contents of video data. Content-based video parsing techniques are presented and analyzed. In order to reduce the computation cost substantially, a non-sequential approach to shot boundary detection is investigated. Efficient browsing and indexing of video data are essential for video data management. Non-linear browsing and cost-effective indexing schemes for video data based on their contents are described and evaluated. In order to satisfy various user requests, delivering long videos through the limited capacity of bandwidth is challenging work. To reduce the demand on this bandwidth, a hybrid of two effective approaches, periodic broadcast and scheduled multicast, is discussed and simulated.
The current techniques related to the above works are discussed thoroughly to explain their advantages and disadvantages, and to make the new improved schemes. The substantial amount of experiments and simulations as well as the concepts are provided to compare the introduced techniques with the other existing ones. The results indicate that they outperform recent techniques by a significant margin. I conclude the dissertation with a discussing of future research directions
PAGaN I: Multi-Frequency Polarimetry of AGN Jets with KVN
Active Galactic Nuclei (AGN) with bright radio jets offer the opportunity to
study the structure of and physical conditions in relativistic outflows. For
such studies, multi-frequency polarimetric very long baseline interferometric
(VLBI) observations are important as they directly probe particle densities,
magnetic field geometries, and several other parameters. We present results
from first-epoch data obtained by the Korean VLBI Network (KVN) within the
frame of the Plasma Physics of Active Galactic Nuclei (PAGaN) project. We
observed seven radio-bright nearby AGN at frequencies of 22, 43, 86, and 129
GHz in dual polarization mode. Our observations constrain apparent brightness
temperatures of jet components and radio cores in our sample to K
and K, respectively. Degrees of linear polarization are
relatively low overall: less than 10%. This indicates suppression of
polarization by strong turbulence in the jets. We found an exceptionally high
degree of polarization in a jet component of BL Lac at 43 GHz, with 40%. Assuming a transverse shock front propagating downstream along the
jet, the shock front being almost parallel to the line of sight can explain the
high degree of polarization.Comment: 14 pages, 17 figures, 4 tables. To appear in JKAS (received 2015 July
27; accepted 2015 October 25). Note the PAGaN II companion paper by J. Oh et
a
PAGaN II: The Evolution of AGN Jets on Sub-Parsec Scales
We report first results from KVN and VERA Array (KaVA) VLBI observations
obtained in the frame of our Plasma-physics of Active Galactic Nuclei (PAGaN)
project. We observed eight selected AGN at 22 and 43 GHz in single polarization
(LCP) between March 2014 and April 2015. Each source was observed for 6 to 8
hours per observing run to maximize the coverage. We obtained a total of
15 deep high-resolution images permitting the identification of individual
circular Gaussian jet components and three spectral index maps of BL Lac, 3C
111 and 3C 345 from simultaneous dual-frequency observations. The spectral
index maps show trends in agreement with general expectations -- flat core and
steep jets -- while the actual value of the spectral index for jets shows
indications for a dependence on AGN type. We analyzed the kinematics of jet
components of BL Lac and 3C 111, detecting superluminal proper motions with
maximum apparent speeds of about . This constrains the lower limits of the
intrinsic component velocities to and the upper limits of the angle
between jet and line of sight to 20. In agreement with global jet
expansion, jet components show systematically larger diameters at larger
core distances , following the global relation , albeit within
substantial scatter.Comment: 13 pages, 15 figures, 4 tables. To appear in JKAS (received 2015
August 31; accepted 2015 October 15). Note the PAGaN I companion paper by
J.-Y. Kim et a
Enhancing Informative Frame Filtering by Water and Bubble Detection in Colonoscopy Videos
Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a novel texture feature based on accumulation of pixel differences, which can detect water and bubble frames with very high accuracy with significantly less processing time. The experimental results show the proposed feature can achieve more than 93% overall accuracy in almost half of the processing time the existing methods take
Interpretable pap smear cell representation for cervical cancer screening
Screening is critical for prevention and early detection of cervical cancer
but it is time-consuming and laborious. Supervised deep convolutional neural
networks have been developed to automate pap smear screening and the results
are promising. However, the interest in using only normal samples to train deep
neural networks has increased owing to class imbalance problems and
high-labeling costs that are both prevalent in healthcare. In this study, we
introduce a method to learn explainable deep cervical cell representations for
pap smear cytology images based on one class classification using variational
autoencoders. Findings demonstrate that a score can be calculated for cell
abnormality without training models with abnormal samples and localize
abnormality to interpret our results with a novel metric based on absolute
difference in cross entropy in agglomerative clustering. The best model that
discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003
area under operating characteristic curve (AUC) and one that discriminates
high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other
clustering methods, our method enhances the V-measure and yields higher
homogeneity scores, which more effectively isolate different abnormality
regions, aiding in the interpretation of our results. Evaluation using in-house
and additional open dataset show that our model can discriminate abnormality
without the need of additional training of deep models.Comment: 20 pages, 6 figure
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