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CSC381/481 INTRODUCTION TO
DIGITAL IMAGE PROCESSING
Components
of an image processing system and its applications, elements of
visual perception, sampling and quantization, image enhancement by
histogram equalization, color spaces and transformations,
introduction to segmentation (edge detection algorithms), and
morphological image processing. Cross-listed with CSC 481.
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CSC382/482 APPLIED IMAGE
ANALYSIS
The course
is meant to provide students with the basic techniques of image
analysis and understanding required for the medical domain, military
domain, new and emerging domains, and other fields of interest to
the students. The topics covered in the course include: imaging
modalities, 2D & 3D imaging, 2D & time-sequenced images, archiving,
accessing and transmitting large images, optic flow, increased
visual discrimination, segmentation, registration, diagnosis,
feature extraction, and image visualization. Prerequisites: CSC 381.
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Edge detection. Image representation and description using
low-level features. A sample of image segmentation techniques.
Perceptual grouping. 2D shape representation and classification.
Motion analysis and tracking. Prerequisites: CSC 381/481.
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Visions Systems will cover the geometry of
computer vision as well as a survey of working vision systems to
include 1) Content-based Image Retrieval Systems; 2) Object
Detection and Tracking Systems; 3) Medical Visual Systems; 4)
Robotic Navigation Systems. Prerequisites: CSC 384/484.
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CSC592 TOPICS
IN COMPUTER SCIENCE AND PATTERN RECOGNITION
Visions Systems will cover the geometry of
computer vision as well as a survey of working vision systems to
include 1) Content-based Image Retrieval Systems; 2) Object
Detection and Tracking Systems; 3) Medical Visual Systems; 4)
Robotic Navigation Systems. Prerequisites: CSC 384/484.
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CSC367
INTRODUCTION TO DATA MINING
The course is an introduction to the Data
Mining (DM) stages and its methodologies. The course provides
students with an overview of the relationship between data
warehousing and DM, and also covers the differences between
database query tools and DM. Possible DM methodologies to be
covered in the course include: multiple linear regression,
clustering, k-nearest neighbor, decision trees, and
multidimensional scaling. These methodologies will be augmented
with real world examples from different domains such as marketing,
e-commerce, and information systems. If time permits, additional
topics may include privacy and security issues in data mining. The
emphasis of this course is on methodologies and applications, not
on their mathematical foundations. Prerequisites: CSC 323
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CSC328/428
DATA ANALYSIS FOR EXPERIMENTERS
The use of statistical software in conducting an analysis of
variance in a variety of settings and the interpretation of
generated results. Analysis of variance for completely randomized,
randomized block, and Latin square designs; for factorial
experiments; for incomplete block designs; with missing data; for
fixed-effects, random-effects, and mixed-effects models; and for
experiments with repeated measures. The analysis of covariance.
PREREQUISITE(S): CSC 324.
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CSC334/424
ADVANCED DATA ANALYSIS
Topics chosen from among multivariate
statistical methods, discriminant analysis, principal components,
factor analysis, discrete multivariate analysis, time series and
non-parametric statistics. PREREQUISITE(S): CSC 324 or consent. |
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IS567 KNOWLEDGE DISCOVERY
TECHNOLOGIES
An introduction
to the Knowledge Discovery in Databases (KDD) technologies
including: data selection and preparation, coding, using a variety
of pattern recognition techniques, and reporting the results. The
course provides information systems students with a comprehensive
overview of data mining and machine learning tools and techniques
that is aimed at maintaining and using databases as a strategic
source of information and knowledge. The course introduces students
to many of the machine learning algorithms including: traditional
statistical algorithms, decision trees, association rules, neural
networks, and genetic algorithms. PREREQUISITE(S): CSC 449 and (CSC
212 or CSC 224).
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