Outline
• Announcements
• Theoretical approaches to computer vision
– Classical theories of vision
– Visual perception as information processing
Announcements
• Class web page
http://www.cs.fsu.edu/~liux/courses/research/index.html
– Lecture notes and papers can be obtained from
http://www.cs.fsu.edu/~liux/courses/research/calendar.html
• Possible programming assignments
– You can certainly do your own project
• Implement a method from the literature
• Implement your own novel ideas on a problem as you are going to
discuss in this class
– You can also do a project on top of my programs
• I will make my programs available to you and you can make changes
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Theoretical Approaches to Vision
• Classical theories of vision
• Visual perception as information processing
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Visual Perception as an Inverse Problem
• Retinal images are generated by the light
reflected from the 3-D world
– The image formation is determined by the laws
of optics
– The area of image rendering is called computer
graphics
• Vision as an inverse problem
– Get from optical images of scenes back to
knowledge of the objects that gave rise to them
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Problems with Inversion
• Image formation is a well-defined function
– Each point in the environment maps into a
unique point in the image
• Inverse process is not well-defined
– Vision goes from 2-D to 3-D
– Each point in the image could map into an
infinite number of points in the environment
– It is underconstrained
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Vision as a Heuristic Process
• Visual system makes a lot of assumptions about the
nature of the environment and conditions under
which it is viewed
– These assumptions constrain the inverse problem enough
to make it solvable most of the time
– The resulting solution will be veridical if the assumptions
are true
– Vision is a heuristic process in which inferences are made
about the most likely environmental condition that could
have produced a given image
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Computer Vision
• The study of how computers can be programmed to
extract useful information about the environment
from the optical images
• Computer metaphor
– Minds are like programs that run on machines called
brains
– Minds are the “software” of biological computation and
the brains are the “hardware”
– Strong AI vs. weak AI
• Artificial intelligence
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Three Levels of Information Processing
• Marr proposed this meta-theory, a theory
about theories on vision
• Three levels of any information processing
systems
– Computational level
– Algorithmic level
– Implementation level
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Computational Level
• Computational level
– The most abstract description level
– Informational constraints available for mapping
input information to output information
– It specifies what computation needs to be
performed and on what information it should be
based
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Algorithmic Level
• Algorithmic level specifies how a
computation is executed in terms of
information processing operations
– There are in principle many different algorithms
to accomplish the computational-level mapping
of input to output
– Two fundamental components
• One must decide a representation for input and output
information
• One must construct a set of processes
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Implementation Level
• This level specifies how an algorithm
actually is embodied as a physical process
within a physical system
– The same algorithm can be implemented using
many physically different devices
• Devices include brains as biological devices as well as
different kinds of computers
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Representations
• A representation refers to a state of the visual
system that stands for an environmental
property, object, or event
– Represented world
• External world outside the information processing
system
– Representing world
• The internal representation within the information
processing system
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Processes
• Processes are the active components in an
information processing system that transform
or operate on information by changing the
representation into the next
– Dynamic aspect of the system that causes
informational transformations to occur
– Implicit vs. explicit
• One of the most important aspects of processes is to
make information that was implicit in the input
representation explicit in the output
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Processing as Inference
• Helmholtz proposed that vision arrives at the
interpretation that is the most likely state of
affairs in the external world that could have
caused the retinal stimulation
– This proposal is called the likelihood principle
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Perception as Bayesian Inference
• Images I are observations
• Scene properties S are not known
• p(S) specifies the prior knowledge about the
scene
– The knowledge you have without looking at the image
• Bayes’ rule
p( S | I ) 
p( I | S ) p( S )
p( I )
p( S | I )  p( I | S ) p( S )
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Top-down vs. Bottom-up Processes
• Bottom-up processing
– Data driven processing
– Take a lower-level representation as input and
create or modify a higher-level representation
• Top-down processing
– Expectation-driven processing
– Processes that take a higher-level representation
as input and produce or modify a lower-level
representation
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Four Stages of Visual Processing
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•
•
•
•
Retinal image
Image-based stage
Surface-based stage
Object-based stage
Category-based stage
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