2007 Graduate Catalog and 2006 Annual R & D Report - Sirindhorn ...

2007 Graduate Catalog and 2006 Annual R & D Report
Sirindhorn International Institute of Technology (SIIT)
School of Information and Computer Technology
Faculty Members and Research Interests, 2007
Dr. Bunyarit Uyyanonvara
Assistant Professor
B.Sc. (1 st Class Honors) in Science (Physics), Prince of Songkhla University, Thailand
Ph.D. in Image Processing, King's College, London, UK
Areas of Specialization: Image processing, Texture segmentation, Relaxation labeling, Medical imaging
Research Interests:
Image Segmentation Using Texture and
Relaxation Labeling Algorithms
When normal density or intensity segmentation is not
effective enough, a new representation of texture
which is derived from the spatial energy of the texture
is introduced in order to segment the given image.
From the energy values, a 2D histogram of texture is
generated. The texture histogram is used to
discriminate textures and to retrieve image
segmentation. In an attempt to assess the similarities
in the regional areas, the property of adjacency could
be useful. This characteristic of pixels is defined as a
co-occurrence matrix, which is an important tool in
Image Segmentation using Texture and Relaxation
Labeling Algorithms.
Medical Image Processing
Taking advantage of the high capability of computers,
offering advantages over film based systems, several
image processing techniques are of interest,
especially for medical purposes in order to get most
of the information out of the given medical images.
Essentially, medical imaging can make use of texture
information, texture feature classification or texture
segmentation because of the nature of the medical
image itself. Medical assessment can then be made
fully automated later on and this will lead to a
reduction of human errors, increasing of consistency
and repeatability. This can be distributed to the
remote areas or hospitals that lack sophisticated
treatment facilities or trained experts.
Dr. Cholwich Nattee
Lecturer
B.Eng. in Computer Engineering, Chulalongkorn University, Thailand
M.Eng. in Computer Science, Tokyo Institute of Technology, Japan
D.Eng. in Computer Science, Tokyo Institute of Technology, Japan
Research Areas: Artificial intelligence, Machine learning, Knowledge discovery and Data mining, Artifcial
Intelligence applications in distance learning and pattern recognition.
Research Interest:
Inductive Logic Programming for Structure-
Activity Relationship Studies
Nowadays, a vast amount of chemical compound
structure information can be produced due to
advances in High Throughput Screening technology
that automates compound screening using the
combination of robotics, image processing and
pattern recognition. From these data, knowledge
describing compound activities and characteristics
from their structures is essential, since it can be used
for predicting characteristics of unknown compounds
for developing new drugs. Machine learning and data
mining techniques have been applied in order to
automatically obtain models describing the relations
between structure and activity. However, traditional
data mining algorithms have limitations on knowledge
representations. Thus, complicated structures of
chemical compounds cannot be handled efficiently.
Extended from traditional machine learning
techniques, Inductive Logic Programming (ILP)
applies first-order logic for representing data. This
allows complicated structures or relations among
training examples to be denoted without losing any
information. Moreover, learning results in the form of
first-order rules, are comprehensible. The knowledge
obtained can be easily explained to domain experts.
Dr. Ekawit Nantajeewarawat
Associate Professor
B.Eng. in Computer Engineering, Chulalongkorn University, Thailand
M.Eng. & D.Eng. in Computer Science, Asian Institute of Technology (AIT), Thailand
Areas of Specialization: Knowledge representation, Computational logics, Formal ontologies, Semantic Web,
Computation theory, Object-oriented system analysis and design.
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