Levels of Realism: From Virtual Reality to Real Virtuality - Perception and Virtual Time
Alan Chalmers (University of Warwick, United Kingdom)
Andrej Ferko (Comenius University, Slovakia)
Realism in real-time has long been a "holy grail" of the
computer
graphics community. While real-time performance is typically accepted
as 25fps and above, the definition of realism remains less
clear. If we were able to simulate the physics of the real world to
minute detail then it would be possible for us to achieve images
which were physically correct. However, the amount of computation
required for such physical accuracy of complex scenes precludes
any possibility of achieving such images in reasonable, let
alone real-time, on a desktop computer for many years to come.
Furthermore, there is no guarantee of realism as these images do
not take into account how the human may perceive this information.
Our perception of an environment is not only what we see,
but may be significantly influenced by other sensory input, including
sound, smell, touch, and even taste. If virtual environments are
ever to be regularly used as a valuable tool to experiment in the
virtual
world with confidence that the results are the same as would
be experienced in the real world, then we need to be able to compute
these environments to be perceptually equivalent as if we were
"there" in the real world; so called "there-reality" or
real virtuality.
We discuss the virtual time issues, as well.
This paper surveys promising efforts to date and identifies and
discusses future research directions.
New Approaches to Fault Tolerant Systems Design
Andreas Steininger (Vienna University of Technology, Austria)
Fault tolerance is achieved by introducing redundancy. Redundancy can appear
in different forms. It can be space redundancy (additional hardware),
information redundancy (additional information helping to verify some data),
and time redundancy (multiple sequential executions of the same code, and/or
execution of additional verification code). Combinations of these redundancy
types are also possible. Fault-tolerant system design based on space
redundancy has a quite long tradition, and many generic architectures and
concepts have been developed that have proven well in traditional
safety-critical application fields like aerospace or (nuclear) power plants.
However, the ongoing introduction of microelectronic systems for
safety-relevant functions in cars is bringing up new problems that cannot be
solved by simply applying the existing approaches. Two main reasons for this
are (i) enormous cost pressure in the automotive industry and (ii) the huge
amount of variants and configuration options for these systems. In my talk I
will report on our experiences in this context. More specifically I will
present a dual-core architecture that we have developed and optimized
together with the automotive industry. I will use this example to touch upon
the topics of error detection in hardware, memory protection, comparison of
(very simple) fault-tolerant computer architectures, common cause faults,
and fault-tolerance evaluation by means of fault injection.
Recent results on DFA minimization and other block splitting algorithms
Antti Valmari (Tampere University of Technology, Finland)
Hopcroft's famous DFA minimization algorithm runs in O( n alpha log n )
time, where n is the number of states and alpha is the number of
different labels. In 2008, an improvement to Hopcroft's algorithm was
published that runs in O( m log n ) time, where m is the number of
transitions. This is an improvement, because m is at most n alpha and
is often much smaller. The improvement was later applied to the
so-called Paige--Tarjan algorithm, yielding an O( m log n ) time
algorithm for bisimulation minimization in the presence of transition
labels. Another improvement, published in 2010, significantly
simplified an existing O( m log n ) time algorithm for optimizing Markov
chains. All these algorithms are block splitting algorithms. This talk
presents these results and some other, small improvements that apply to
block splitting algorithms.
Model-Based Segmentation of Biomedical Images
Stefan Wörz (University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Germany)
A central task in biomedical image analysis is the segmentation and
quantification of 3D image structures. A large variety of segmentation
approaches is based on deformable models. Deformable models allow, for
example, to incorporate a priori information about the image structures.
This talk gives an overview of different types of deformable models such as
active contour models, active shape models, active appearance models, and
analytic parametric models. Moreover, this talk presents in more detail 3D
parametric intensity models, which are utilized in different approaches for
high-precision localization and quantification of 3D image structures. In
these approaches, the intensity models are used in conjunction with an
accurate, efficient, and robust model fitting scheme. These segmentation
approaches have been successfully applied to different biomedical
applications such as the localization of 3D anatomical point landmarks in 3D
MR and 3D CT images, the quantification of vessels in 3D MRA and 3D CTA
images, as well as the segmentation and quantification of cells and
subcellular structures in 3D microscopy images.
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