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11th International Conference on Information
Visualisation - IV07 2nd International Conference Geometric Modelling and Imaging
- GMAI07
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Robert
Spence FREng, 2.
Usability Evaluation and
Information Visualization Keith
Andrews, GMAI07
Tutorials: 3.
TCM Based Distributed
Video Coding for Low Cost Video
Encoding Anil
Fernando, 4.
Moments and Moments
Invariants in Image Analysis Jan
Flusser, Barbara Zitova, and Tomas Suk, Institute of Information Theory
and Automation, |
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A Full-day Course: Tuesday 3rd
July 2007, Time: 10:30 -17:30
Information Visualization
Professor Robert Spence
FREng,
Abstract
You have data. Lots of it. And an
uneasy feeling that, hidden within it, are some insights that can benefit your
business as well, perhaps, as some nasty surprises you'd like to be forewarned
about. The solution? Turn your data into pictures, explore it
interactively, and eventually say "Ah ha! . . . ". We're
talking about the acquisition of insight into data without the need for
expertise in statistics, mathematics or computing. That's what
Information Visualization is all about.
Organisation
There
are three topics that underlie the discipline of Information
Visualization. They are Representation (the visual encoding of data),
Presentation (how that represented data is laid out on a display), and
Interaction (how interactive modification of the data's representation can
enhance insight). Those three topics form the main structure of the
tutorial, but will be preceded by an Introduction and followed by some Case
Studies illustrating the application of information visualization.
Level of Tutorial: Introductory
level
Biography
Bob Spence
is Emeritus Professor of Information Engineering at Imperial College London
where he has carried out research into Human-computer Interaction, and
Information Visualization in particular, since 1968. He has lectured very
extensively around the world and has recently published the second edition of
his text Information Visualization: Design for Interaction (Prentice Hall
2007). He and his group have been responsible for some of the major
inventions in the field of Information Visualization including the distortion
approach to the Focus+Context problem and the Attribute and Influence
Explorers. Bob is a Fellow of the Royal Academy of Engineering.
NOTE:
The
second edition of Bob Spence book on Information Visualization: Design for
Interaction (Prentice Hall 2007) will be available with this course.
Tutorial
Cost: [BEFORE 30th April 2007 ] £110; [AFTER 30th
April 2007 ] £120
Tutorial Cost
<discounted rate for the conference delegates>: [BEFORE 30th April 2007 ] £100;
[AFTER
30th April 2007 ] £110
A Full-day Course: Tuesday 3rd
July 2007, Time: 10:30 -17:30
Distributed
Video Coding for Low Cost Video Encoding
Anil
Fernando,
Abstract
Video
Coding technologies have evolved tremendously over past decades in line with
the rapidly increasing demand over vastly expanding application domains. The
research on video coding has been traditionally dominated by the work on MPEG
and ITU-T H.26x standardisations based systems. It is known that in these
mainstream technologies the video encoders are far more complex (by approximately
5 to 10 times) than the decoder structure. This architecture was motivated by
many of the conventional one-to-many type video applications including
broadcasting (DVB), video streaming etc where the decoder cost need to
maintained considerably low for the benefit of large numbers of viewers
compared to the limited number of content providers. However, more recently,
this architecture is challenged by new consumer applications where the cost of
encoder is a prime concern due to the necessity of vast deployments of video
sensors. Security surveillance systems, mobile video conferencing, monitoring
of the disabled people and children, disaster zone monitoring are a few
potential scenarios which are largely benefited by massive encoder deployments.
Distributed Video Coding (DVC) is an emerging video coding technology designed
with a modified complexity balance between the encoder and decoder in line with
the necessities of these applications. The dramatically low complexity of the
DVC encoder helps these solutions by: (i) reducing the production cost of the
signal processors of the video sensors, (ii) reducing the requirement of
digital memory and (iii) reducing the power consumption which is generally a
scarce resource at remote sites.
The research
on DVC is still in preliminary stages and considerable amount of effort is
necessary before going through the standardisation process and commercial use.
The currently available literatures have used a number of hypothetical models
and assumptions some of which have not yet been assessed for practical
viability. Key frame transmission algorithm, error and noise distribution
estimation at the decoder, implementation of the reverse feedback channel using
the dynamic error estimation and the necessary communication protocols are some
of the major open areas for research in DVC. Side information generation is an
area largely discussed in literature, yet further room for development.
In
DVC, the shift of complexity balance is achieved by moving the major task of
the exploitation of source correlations to achieve the compression into the
decoder. This task involves the generation of a representation of a part of
original sequence called side information. A sequence of ‘selected’ original
frames is generally passed to the decoder over the channel using an intra frame
coding scheme and are called ‘key-frames’. The frequency of key-frame
transmission could vary on the DVC implementation strategy. The missing frames
are estimated at the decoder using interpolation/extrapolation techniques or
more complex and accurate motion prediction methods. It is assumed that the
side information so generated is a form of the original sequence subjected to
noise while transmission. The identification of the statistical distribution of
this ‘noise’ is a part of the ongoing research activity. The side information
used for processing with the parity information sequence transmitted over the
channel by the encoder. At the encoder this parity bits are generated by
passing the original video sequence through a set of shift registers and logic
gates. Further this parity bit sequence is generally subjected to puncturing,
of which the rate determines the channel bandwidth requirement and it varies on
the implementation strategy for a given quality of image reconstruction at the
decoder.
The
number of bits transmitted over the channel to represent each pixel (bpp) and
the closeness of the reconstructed image at the decoder to the original frame
held back at the encoder (PSNR) are the common measures of the goodness of the
video codec implementation. The theoretical base and the guidelines for
Distributed Source Coding were set by Slepian-Wolf and the current work in this
field is based on the work by Wyner-Ziv.
In
this tutorial we will discuss the motivations behind the DVC design, current
DVC codec architecture and possible modifications to enhance the performance,
The hypothetical models and assumptions used in the current design together
design criteria for possible practical solutions and some of the potential
application domains.
List of achievements
- Brief overview to distributed source coding.
- Concept of DVC.
- The architecture of the DVC codec based on turbo
coding.
- Preparation of input bitstream using quantization & bitplane extraction for turbo
encoding.
- Puncturing the parity bit stream for video
compression.
- Side information generation using key-frames.
- Turbo decoding using side information and parity
from encoder.
- Reconstruction function.
- Modifications to the current DVC codec
architecture.
- The hypothetical assumptions and models used in
the current architecture and designing practical solutions.
- Statistical Modeling of Distribution estimation
at the decoder.
- Dynamic error estimation at the decoder.
- Video communications over noisy/wireless channels
using DVC.
- Applications and business Models.
- Future of the distributed Video Coding.
Audience:
The
potential beneficiaries of this tutorial will include the researchers, equipment
manufacturers and other interested segments from the industry in the fields of
wireless and multimedia communications. We will discuss a feasible solution for
effective and efficient use of wireless media for communications in the
areas of security surveillance, disaster zone
monitoring, design of realistic entertainment systems, corporate
communications, telemedicine, telecommuting, distance learning, sales and
customer service, remote support, career services, video justice, and emergency
services. The successful completion of this tutorial will provide a
framework for the future distributed video communications products and will
contribute to possible standards activities. Architectural aspects of this work
will be of use to VLSI manufacturers.
Dr. W.A.C. Fernando
(SMIEEE) leads the Video Codec group within the Networks and Multimedia
Communication Systems Centre in
He is a member of the editorial board of the international journal of
multimedia tools and applications. He has also been nominated as the guest
editor for the special issue on joint source and channel coding for multimedia
communications of the international journal of multimedia. Furthermore, he has
been working as a referee for IEEE Transactions on Circuits and Systems for
Video Technology, IEEE Transactions on Communications, Mobile Computing,
Communications Letters, IEE proceedings of communications, IEE proceedings of
Vision and Computing, IEE Electronic Letters, Journal of Communications
Networking, Electronics and Telecommunications Research journal, SPIE journals,
etc., and many conference proceedings (VTC, ICC, ISCAS, ICIP, SPIE, ITC,
etc.,).
A Full-day Course: Tuesday 3rd
July 2007, Time: 10:30 - 17:30
"Usability
Evaluation and Information Visualization"
Professor
Keith Andrews,
Abstract
The
first part of this tutorial will look at usability evaluation in general. It will
cover both usability inspection methods (such as heuristic evaluation,
cognitive walkthrough, and action analysis) and usability testing methods (such
as thinking aloud tests, formal experiments, and usage studies).
The
second part of the tutorial will look specifically at the evaluation of
information visualisations. We will step through the design, analysis, and
reporting of two comparative studies of information visualisation techniques.
Organisation
Level of Tutorial: Introductory level
Keith Andrews is a tenured associate
professor at the Institute for Information Systems and Computer Media (IICM) at
Graz University of Technology, in
His
research interests include information visualisation, human-computer
interaction, and the internet. He has a B.Sc.(Hons) in Mathematics and Computer
Science from the University of York, England, and an M.Sc. and Ph.D. in
Technical Mathematics/Computer Science from Graz University of Technology.
Having
lead the Harmony (Unix/X11 browser for Hyperwave) and VRwave (VRML browser)
projects for several years, he is currently pursuing research in the field of
information visualisation. Keith was program co-chair of the IEEE Symposium on
Information Visualization in 2001 and 2002, and general chair in 2005.
Keith
teaches undergraduate level courses on Internet and New Media, Human-Computer
Interaction, and User Interface Design, and graduate level courses on
Information Visualisation and Information Architecture and Web Usability, as
well as various short courses and tutorials at conferences and for companies.
A Full-day Course: Tuesday 3rd
July 2007, Time: 10:30 - 17:30
MOMENTS AND
MOMENT INVARIANTS IN IMAGE ANALYSIS
Jan Flusser, Barbara
Zitova, and Tomas Suk
Email:
flusser@utia.cas.cz
Webpage of this
tutorial: http://staff.utia.cas.cz/zitova/tutorial/index.html
Abstract
This
tutorial aims to present a survey of recent as well as traditional image
analysis and recognition methods based on image moments. We review various
types of moments (geometric moments, complex moments, Legendre moments, Zernike
and Pseudo-Zernike moments, and Fourier-Mellin moments) and moment-based
invariants with respect to various image degradations and distortions
(rotation, scaling, affine transform, image blurring, etc.) which can be used
as shape descriptors for classiffication. Practical examples from various
application areas (character recognition, medical imaging, remote sensing,
robot vision) will be demonstrated.
The
target audience of the tutorial are:
- Researchers from
all application areas who need to recognize 2-D objects extracted from
binary/graylevel/color images and who look for invariant and robust object
descriptors.
- Specialists in
moment-based image analysis interested in a new development on this field.
Description of the tutorial
Recognition/classification
of images and patterns independently of their position, size, orientation and
other variations in geometry and colors has been the goal of much recent
research. Finding efficient invariant object descriptors is the key to solving
this problem. Several groups of features have been used for this purpose, such
as simple visual features (edges, contours, textures, etc.), Fourier and
Hadamard coefficients, differential invariants, and moment invariants, among
others.
This
tutorial is devoted to the history, recent advances and prospective future
development of moment invariants and moment-based image analysis. Moments were
originally introduced to the pattern recognition community in 1962 by M.K. Hu,
who employed the theory of algebraic invariants and derived his seven famous
invariants to rotation of 2-D objects. Since that time, moment invariants have
become a classical tool for feature-based object recognition. Many research
papers have been devoted to various improvements and generalizations of the
Hu's invariants and to their utilization in many application areas as well as
to developing other systems of moment invariants.
This
tutorial originates from 20-years speakers' experiences in moments, moment
invariants, and related fields. The tutorial covers mainly the following
topics.
- Rotation moment
invariants from higher-order moments
- Affine moment
invariants
- Invariants to linear
filtering
- Combined invariants
- Orthogonal moments
- Algorithms for moment
computation
- Applications
Jan
Flusser received the M.Sc. degree in mathematical engineering from the
He
has authored and coauthored more than 100 research publications, tutorials and
invited talks in these areas. About 70 his publications are relevant to the
topic of the proposed tutorial. Some of his journal papers became classical and
are frequently cited. Jan Flusser is a Senior Member of the IEEE.
Barbara
Zitova received the M.Sc. degree in computer science from the
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