All technical report files are in compressed (gzip) postscript format.
The file names are of the sort BU-CE-YYXX.pdf
BU-CE-0301
TITLE: Maximizing Benefit of Classifications Using Feature Intervals
AUTHORS: Nazli Ikizler and H. Altay Güvenir
ABSTRACT:
There is a great need for classification methods that can
properly handle asymmetric cost and benefit constraints of
classifications. In this study, we aim to emphasize the importance of
classification benefits by means of a new classification algorithm,
Benefit Maximizing classifier with Feature Intervals (BMFI) that uses
feature projection based knowledge representation. Empirical results
show that BMFI has promising performance compared to recent
cost-sensitive algorithms in terms of benefit gained.
BU-CE-0302
TITLE: Extraction of 3D Navigation Space In Virtual Urban Environments
AUTHORS: Turker Yilmaz and Ugur Gudukbay
ABSTRACT:
Urban scenes are one class of complex geometrical
environments in computer graphics. In order to develop navigation
systems for urban sceneries, extraction and cellulization of
navigation space is one of the most commonly used technique
providing a suitable structure for visibility computations.
Surprisingly, there is not much work done for the extraction of
the navigable area automatically. Urban models, except the ones
where the building footprints are used to generate the model,
generally lack of navigation space information. Because of this,
it is hard to extract and discretize the navigable area for
complex urban scenery. In this paper, we propose an algorithm for
the extraction of navigation space for urban scenes in
three-dimensions (3D). Our navigation space extraction algorithm
works for scenes, where the buildings are in high complexity and
the virtual scene is constructed by populating these buildings
without making any assumptions. The building models may have
pillars or holes where seeing through them is also possible.
Besides, for the urban data acquired from different sources which
may contain errors, our approach provides a simple and efficient
way of discretizing both navigable space and the model itself.
Furthermore, terrain height field information can be extracted
from the resultant structure, hence providing a way to implement
urban navigation systems including terrains.
Keywords:
Urban visualization, occlusion culling, cellulization, 3D navigation,
view-cells.
BU-CE-0304
TITLE: Feature Dependency in Benefit Maximization: A Case Study in the
Evaluation of Bank Loan Applications
AUTHORS: Nazli Ikizler and H. Altay Güvenir
ABSTRACT:
In most of the real-world domains, benefit and costs of
classifications can be dependent on the characteristics of individual
examples. In such cases, there is no static benefit matrix available
in the domain and each classification benefit is calculated
separately. This situation, called feature dependency, is evaluated in
the framework of our newly proposed classification algorithm Benefit
Maximizing classifier with Feature Intervals (BMFI) that uses feature
projection based knowledge representation. This new approach has been
evaluated over bank loan applications and experimental results are presented.
Keywords:
Machine learning, classification, cost-sensitivity, feature projections.
BU-CE-0305
TITLE: Feature Projection Based Rule Classification
AUTHORS: Tolga Aydin and H. Altay Güvenir
ABSTRACT:
Due to the increase in data mining research and applications,
selection of interesting rules among a huge number of learned rules is
an important task in data mining applications. In this paper, the
metrics for the interestingness of a rule is investigated and an
algorithm that can classify the learned rules according to their
interestingness is developed. Classification algorithms were designed
to maximize the number of correctly classified instances, given a set
of unseen test cases. Furthermore, feature projection based
classification algorithms were tested and shown to be successful in
large number of real domains. So, in this work, a feature projection
based classification algorithm (VFI, Voting Feature Intervals) is
adapted to the rule interestingness problem, and FPRC (Feature
Projection Based Rule Classification) algorithm is developed.
Keywords:
Rule classification, interestingness, voting, feature projection.
BU-CE-0306
TITLE: Realistic Rendering of a Multi-Layered Human Body Model
AUTHORS: Mehmet Sahin Yesil and Ugur Güdükbay
ABSTRACT:
In this thesis study, a framework is proposed and implemented for
the realistic rendering of a multi-layered human body model while
it is moving. The proposed human body model is composed of three
layers: a skeleton layer, a muscle layer, and a skin layer. The
skeleton layer, represented by a set of joints and bones, controls
the animation of the human body model using inverse kinematics.
Muscles are represented by action lines, which are defined by a
set of control points. The action line expresses the force
produced by a muscle on the bones and on the skin mesh. The skin
layer is modeled in a 3D modeler and deformed during animation by
binding the skin layer to both the skeleton layer and the muscle
layer. The skin is deformed by a two-step algorithm according to
the current state of the skeleton and muscle layers. In the first
step, the skin is deformed by a variant of the skinning algorithm,
which deforms the skin based on the motion of the skeleton. In the
second step, the skin is deformed by the underlying muscular
layer. Visual results produced by the implementation is also
presented. Performance experiments show that it is possible to
obtain real-time frame rates for a moderately complex human model
containing approximately 33,000 triangles on the skin layer.
Keywords:
Human body modeling and animation,
multi-layered modeling, articulated figure, kinematics, inverse
kinematics, action line, skinning, deformation.
BU-CE-0307
TITLE: Human Motion Control Using Inverse Kinematics
AUTHORS: Aydemir Memisoglu, Bülent Özgüç and Ugur Güdükbay
ABSTRACT:
Articulated figure animation receives particular attention of the
computer graphics society. The techniques for animation of
articulated figures range from simple interpolation between
keyframes methods to motion-capture techniques. One of these
techniques, inverse kinematics, which is adopted from robotics,
provides the animator the ability to specify a large quantity of
motion parameters that results with realistic animations. This
study presents an interactive hierarchical motion control system
used for the animation of human figure locomotion. We aimed to
develop an articulated figure animation system that creates
movements using motion control techniques at different levels,
like goal-directed motion and walking. Inverse Kinematics using
Analytical Methods (IKAN) software, which was developed at the
University of Pennsylvania, is utilized for controlling the motion
of the articulated body using inverse kinematics.
Keywords:
kinematics, inverse kinematics,
articulated figure, motion control, spline, gait.
BU-CE-0308
TITLE: Benefit Maximization in Classification on Feature Projections
AUTHOR: H. Altay Güvenir
ABSTRACT:
In some domains, the cost of a wrong classification may be different
for all pairs of predicted and actual classes. Also the benefit of a
correct prediction is different for each class. In this paper, a new
classification algorithm, called BCFP (for Benefit Maximizing
Classifier on Feature Projections), is presented. The BCFP classifier
learns a set of classification rules that will predict the class of a
new instance with maximum benefit or minimum cost. BCFP represents a
concept in the form of feature projections on each feature dimension
separately. Classification in the BCFP algorithm is based on a voting
among the individual predictions made on each feature. A genetic
algorithm is used to select the relevant features. The performance of
the BCFP algorithm is evaluated in terms of accuracy. As a case study,
the BCFP algorithm is applied to the problem of diagnosis of gastric
carcinoma. A lesion can be an indicator of one of nine different
levels of gastric carcinoma. The benefit of correct classification of
early levels is much more than that of late cases. Also, the cost of
wrong classifications is different for all classes.
Keywords:
Machine learning, feature projection, voting, benefit maximization.
BU-CE-0309
TITLE: Learning Translation Templates for Closely Related Languages
AUTHOR: Kemal Altintas and H. Altay Güvenir
ABSTRACT:
Many researchers have worked on example-based machine translation and
different techniques have been investigated in the area. In
literature, a method of using translation templates learned from
bilingual example pairs was proposed. The paper investigates the
possibility of applying the same idea for close languages where word
order is preserved. In addition to applying the original algorithm for
example pairs, we believe that the similarities between the translated
sentences may always be learned as atomic translations. Since the word
order is almost always preserved, there is no need to have any
previous knowledge to identify the corresponding differences. The
paper concludes that applying this method for close languages may
improve the performance of the system.
BU-CE-0310
TITLE: Vision Based Handwritten Character Recognition
AUTHOR: Özcan Öksüz
ABSTRACT:
Onine automatic recognition of handwritten text has been
an onoing research problem for four decades. It is used in automated
postal address and ZIP code and form reading, data acquisition in bank
checks, processing of archived institutional records, automatic
validation of passports, etc. It has been gaining more interest lately
due to the increasing popularity of handeld computers, digital
notebooks and advanced cellular phones. Traditionally, human-machine
communication has been based on keyboard and pointing devices. Onine
handwriting recognition promises to provide a dynamic means of
communication with computers through a pen like stylus, not just an
ordinary keyboard. This seems to be a more natural way of entering
data into computers.
In this thesis, we develop a character recognition system that
combines the advantage of both on-line and off-line systems. Using an
USB CCD Camera, positions of the pen-tip between frames are detected
as they are written on a sheet of regular paper. Then, these positions
are used for calculation of directional information. Finally,
handwritten character is characterized by a sequence of writing
directions between consecutive frames. The directional information of
the pen movement points is used for character pre-classification and
positional information is used for fine classification. After
characters are recognized they are passed to LaTeX code generation
subroutine. Supported LaTeX environments are array construction,
citation, section, itemization, equation, verbatim and normal text
environments. During experiments a recognition rate of 90% was
achieved. The main recognition errors were due to the abnormal writing
and ambiguity among similar shaped characters.
Keywords:
pattern recognition, character Recognition, on-line recognition
systems, LaTeX.
BU-CE-0311
TITLE: Using a Data Mining approach for Prediction of User Movements
in Mobile Environments
AUTHOR: Gökhan Yavas
ABSTRACT:
Mobility prediction is one of the most essential issues that need to
be explored for mobility management in mobile computing systems. In
this thesis, we propose a new algorithm for predicting the next
inter-cell movement of a mobile user in a Personal Communication
Systems network. In the first phase of our three-phase algorithm, user
mobility patterns are mined from the history of mobile user
trajectories. In the second phase, mobility rules are extracted from
these patterns, and in the last phase, mobility predictions are
accomplished by using these rules. The performance of the proposed
algorithm is evaluated through simulation as compared to two other
prediction methods. The performance results obtained in terms of
Precision and Recall indicate that our method can make more accurate
predictions than the other methods.
Keywords:
Location prediction, data mining, mobile computing, mobility patterns,
mobility prediction.