Outline
• Syllabus
• Introduction
• Computational Paradigms for Vision
– Appearance-based computer vision
– Physics-based computer vision
Textbooks
• Textbooks
– I will use chapters from different books and papers
from the literature
– Most of the books were used in other classes
– The book “2D Object Detection and Recognition” is
available
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The Goal of This Class
• The focus of this class is not the formality
– I will go through the basic concepts in the first
few weeks
– Then we will focus on important research topics
one by one using one or few key papers
• After discussing each of the key papers, we need to be
crystal clear about the problems and the approaches so
that we will understand whenever we see them again
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Vision
• Vision
– The process of acquiring knowledge about the
environmental objects and events by extracting
information from the light they emit or reflect
– Vision is a very complicated process, involving
different processes such as memory
– Vision is the most useful source for information
as about 50% of the human brain is devoted to
visual processing
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Vision – cont.
• Vision has been studied from many different
perspectives
– Computational vision
• Emphasis on approaches that are biologically
plausible
– Computer vision
• Emphasis on algorithms to solve particular problems
– Statistical vision
• Emphasis on developing and analyzing mathematical
and statistical models
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What is the Truth?
• In the context of learning, what is the truth?
– Statistical / mathematical frameworks or
– Effective and efficient algorithms?
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Transduction Inference
A principle advocated by Vladimir Vapnik
“If you are limited to a restricted amount of information, do not solve
the particular problem you need by solving a more general problem”
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