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Visual Informatics: Sustaining Research and Innovations - Second International Visual Informatics Conference, IVIC 2011, Selangor, Malaysia, November 9-11, 2011, Proceedings, Part I (Paperback, 2011 ed.)
Halimah Badioze Zaman, Peter Robinson, Maria Petrou, Patrick Olivier, Timothy K. Shih, …
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R1,588
Discovery Miles 15 880
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Ships in 10 - 15 working days
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The two-volume set LNCS 7066 and LNCS 7067 constitutes the
proceedings of the Second International Visual Informatics
Conference, IVIC 2011, held in Selangor, Malaysia, during November
9-11, 2011. The 71 revised papers presented were carefully reviewed
and selected for inclusion in these proceedings. They are organized
in topical sections named computer vision and simulation; virtual
image processing and engineering; visual computing; and
visualisation and social computing. In addition the first volume
contains two keynote speeches in full paper length, and one keynote
abstract.
The field of machine learning and data mining in connection with
pattern recognition enjoys growing popularity and attracts many
researchers. Automatic pattern recognition systems have proven
successful in many applications. The wide use of these systems
depends on their ability to adapt to changing environmental
conditions and to deal with new objects. This requires learning
capabilities on the parts of these systems. The exceptional
attraction of learning in pattern recognition lies in the specific
data themselves and the different stages at which they get
processed in a pattern recognition system. This results a specific
branch within the field of machine learning. At the workshop, were
presented machine learning approaches for image pre-processing,
image segmentation, recognition and interpretation. Machine
learning systems were shown on applications such as document
analysis and medical image analysis. Many databases are developed
that contain multimedia sources such as images, measurement
protocols, and text documents. Such systems should be able to
retrieve these sources by content. That requires specific retrieval
and indexing strategies for images and signals. Higher quality
database contents can be achieved if it were possible to mine these
databases for their underlying information. Such mining techniques
have to consider the specific characteristic of the image sources.
The field of mining multimedia databases is just starting out. We
hope that our workshop can attract many other researchers to this
subject.
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Visual Informatics: Sustaining Research and Innovations - Second International Visual Informatics Conference, IVIC 2011, Selangor, Malaysia, November 9-11, 2011, Proceedings, Part II (Paperback, 2011 ed.)
Halimah Badioze Zaman, Peter Robinson, Maria Petrou, Patrick Olivier, Timothy K. Shih, …
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R1,606
Discovery Miles 16 060
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Ships in 10 - 15 working days
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The two-volume set LNCS 7066 and LNCS 7067 constitutes the
proceedings of the Second International Visual Informatics
Conference, IVIC 2011, held in Selangor, Malaysia, during November
9-11, 2011. The 71 revised papers presented were carefully reviewed
and selected for inclusion in these proceedings. They are organized
in topical sections named computer vision and simulation; virtual
image processing and engineering; visual computing; and
visualisation and social computing. In addition the first volume
contains two keynote speeches in full paper length, and one keynote
abstract.
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Visual Informatics: Bridging Research and Practice - First International Visual Informatics Conference, IVIC 2009 Kuala Lumpur, Malaysia, November 11-13, 2009 Proceedings (Paperback, 2009 ed.)
Halimah Badioze Zaman, Peter Robinson, Maria Petrou, Patrick Olivier, Heiko Schroeder
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R4,688
Discovery Miles 46 880
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Ships in 10 - 15 working days
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Visual informatics is a field of interest not just among the
information technology and computer science community, but also
other related fields such as engineering, me- cal and health
informatics and education starting in the early 1990s. Recently,
the field is gaining more attention from researchers and industry.
It has become a mul- disciplinary and trans-disciplinary field
related to research areas such as computer vision, visualization,
information visualization, real-time image processing, medical
image processing, image information retrieval, virtual reality,
augmented reality, - pressive visual mathematics, 3D graphics,
multimedia-fusion, visual data mining, visual ontology, as well as
services and visual culture. Various efforts has been - vested in
different research, but operationally, many of these systems are
not pro- nent in the mass market and thus knowledge and research on
these phenomena within the mentioned areas need to be shared and
disseminated. It is for this reason that the Visual Informatics
Research Group from Universiti - bangsaan Malaysia (UKM) decided to
spearhead this initiative to bring together experts in this very
diversified but important research area so that more concerted
efforts can be undertaken not just within the visual informatics
community in Malaysia but from other parts of the world, namely,
Asia, Europe, Oceania, and USA. This first International Visual
Informatics Conference (IVIC 2009) was conducted collaboratively,
by the visual informatics research community from the various
public and private institutions of higher learning in Malaysia, and
hosted by UKM.
We present a new method for the segmentation and the detection of
human Abdominal Aorta in CT images. Our method is divided into two
parts. In the first part we estimate the position and the dimension
of the aortic lumen using state-of-the-art object tracking
techniques. The second part employs curve fitting methods in order
to detect the boundaries of the aortic lumen with accuracy, based
on the estimation of the first part. In particular, the proposed
method uses the Kalman Filter to track the aortic cross-section in
consecutive CT images. The observations needed by the Kalman
procedure are extracted with the Circle Hough Transformation, based
on the assumption that the morphological structure of the aortic
cross-section is approximately a circle. A robust Level Set method
is then applied to compensate the approximation error and
efficiently estimate the cross-section. The algorithms and the
mathematical tools developed during the project prove feasibility
for an accurate and reliable method for the segmentation of the
abdominal aorta from CT data, that in the future could be used to
benefit patients with aortic aneurysms.
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