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Artificial
Intelligence (CS607)
9 Advanced
Topics
9.1 Computer
vision
It is a subfield
of Artificial Intelligence. The purpose
of computer vision is to
study
algorithms,
techniques and applications
that help us make machines
that can
"understand" images
and videos. In other words,
it deals with procedures
that
extract
useful information from
static pictures and sequence
of images. Enabling
a machine to
see, percieve and understand
exactly as humans see,
percieve and
understand is
the aim of Computer
Vision.
Computer
vision finds its
applications in medicine, military,
security and
surveillance,
quality inspection, robotics,
automotive industry and many
other
areas.
Few areas of vision in which
research is benig actively
conducted
thoughout
the world are as
follows:
The detection, segmentation, localisation, and recognition of
certain
objects in
images (e.g., human
faces)
Tracking
an
object through an image
sequence
Object
Extraction from a
video sequence
Automated Navigation
of a
robot or a vehicle
Estimation
of
the three-dimensional pose of humans and
their limbs
Medical Imaging, automated
analysis of different body
scans (CT Scan,
Bone
Scan, X-Rays)
Searching
for
digital images by their
content (content-based
image
retrieval)
Registration
of
different views of the same
scene or object
Computer
vision encompases topics
from pattern recognition,
machine learning,
geometry,
image processing, artificial
intelligence, linear algebra
and other
subjects.
Apart
from its applications,
computer vision is itself
interesting to study. Many
detailed
turorials regarding the
field are freely avalible on
the internet. Readers
of
this
text are encouraged to read
though these tutorials get
indepth knowledge
about
the limits and contents of
the field.
Exercise
Question
Search
though the internet and read
about intersting happeneing
and reseach
going on
around the globe in the
are of Computer
Vision.
203
Artificial
Intelligence (CS607)
http://www.cs.ucf.edu/~vision/
The above
link might be useful to
explore knowledge about
computer vision.
9.2
Robotics
Robotics is
the highly advanced and
totally hyped field of
today. Literally
speaking,
robotics is the study of
robots. Robots are nothing
but a complex
combination of
hardware and intelligence, or mechanics
and brains. Thus
robotics is
truly a multi-disciplinary area,
having active contributions
from,
physics,
mechanics, biology, mathematics,
computer science, statistics,
control
thory,
philosophy, etc.
The features
that constitute a robot
are:
Mobility
·
Perception
·
Planning
·
Searching
·
Reasoning
·
Dealing
with uncertainty
·
Vision
·
Learning
·
Autonomy
·
Physical
Intelligence
·
What we
can see from the
list is that robotics is the
most profound
manifestation
of AI in practice.
The most crucial or defining
ones from the list
above are
mobility,
autonomy and dealing with
uncertainety
The area of
robotics have been followed
with enthusiasm by masses
from fiction,
science and
industry. Now robots have
entered the common
household, as robot
pets
(Sony Aibo entertainment
robot), oldage assistant and
people carriers
(Segway
human transporter).
Exercise
Question
Search
though the internet and read
about intersting happeneing
and reseach
going on
around the globe in the
are of robotics.
http://www.cs.dartmouth.edu/~brd/Teaching/AI/Lectures/Summaries/robotics.html
The above
link might be useful to
explore knowledge about
robotics.
9.2.1
Softcomputing
Softcomputing is a
relatively new term coined to
encapsulate the emergence
of
new
hybrid area of work in AI.
Different technologies including
fuzzy systems,
204
Artificial
Intelligence (CS607)
genetic
algorithms, neural networks
and a few statistical
methods have been
combined
together in different orientations to
successfully solve today's
complex
real-world
problems.
The most
common combinations are of
the pairs
· genetic
algorithms fuzzy systems
(genetic fuzzy)
· Neural
Networks fuzzy systems
(neuro-fuzzy systems)
· Genetic
algorithms Neural Networks
(neuro-genetic systems)
Softcomputing is
naturally applied in machine
learning applications. For
example
one usage of
genetic-fuzzy system is of `searching'
for an acceptable
fuzzy
system
that conforms to the
training data. In which,
fuzzy sets and
rules
combined,
are encoded as individuals,
and GA iterations refine the
individuals
i.e.
fuzzy system, on the basis
of their fitness evaluations. The
fitness function is
usually MSE of
the invidual fuzzy system on
the training data. Very
similar
applications
have been developed in the
other popular neuro-fuzzy
systems, in
which
neural networks are used to
find the best fuzzy
system for the given
data
through
means of classical ANN learning
algorithms.
Genetic
algorithms have been
employed in finding the
optimal initial weights
of
neural
networks.
Exercise
Question
Search
though the internet and read
about intersting happeneing
and reseach
going on
around the globe in the
are of softcomputing.
http://www.soft-computing.de/
The above
link might be useful to
explore knowledge about
softcomputing.
9.3
Clustering
Clustering is a
form of unsupervised learning, in
which the training data
is
available
but without the
classification information or class
labels. The task of
clustering is to
identify and group similar
individual data elements
based on some
measure of
similarity. So basically using
clustering algorithms,
classification
information
can be `produced' from a
training data which has no
classification
data at
the first place. Naturally,
there is no supervision of classification
in
clustering
algorithms for their
learning/clustering, and hence they
fall under the
category of
unsupervised learning.
The famous
clustering algorithms are
Self-organizing maps (SOM),
k-means,
linear
vector quantization, Density
based data analysis,
etc.
Exercise
Question
205