Artificial Intelligence
Past, Present, and Future
Olac Fuentes
Associate Professor
Computer Science Department
UTEP
1
Artificial Intelligence
A definition:
• AI is the science and engineering of making
intelligent machines
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Artificial Intelligence
A definition:
• AI is the science and engineering of making
intelligent machines
But, what is intelligence?
• A very general mental capability that, among other
things, involves the ability to reason, plan, solve
problems, think abstractly, comprehend complex
ideas, learn quickly, and learn from experience.
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Artificial Intelligence
Another definition:
• AI is the science and engineering of making
machines that are capable of:
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–
–
–
–
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Reasoning
Representing knowledge
Planning
Learning
Understanding (human) languages
Understanding their environment
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Artificial Intelligence
• Weak AI Claim - Machines can possibly act as if
they were intelligent
• Strong AI Claim - Machines can actually think
intelligently
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Artificial Intelligence
Why?
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Artificial Intelligence
Why?
– Building an intelligent machine will help
us better understand natural intelligence
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Artificial Intelligence
Why?
– Building an intelligent machine will help
us better understand natural intelligence
– An intelligent machine can be used to
perform difficult and useful tasks whether it models human intelligence or
not
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Artificial Intelligence
Brief History
– Field was founded in 1956, initially led by John Mc
Carthy, Marvin Minsky, Allen Newell and Herbert
Simon (known as the “fourfathers” of A.I.)
– Great initial optimism, grandiose objectives
(“machines will be capable, within twenty years, of
doing any work a man can do” – H. Simon)
– Emphasis on symbolic reasoning
– Huge government spending
– Disappointing results
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Artificial Intelligence - Brief History
The A.I. Winter
– 1966: the failure of machine translation,
– 1970: the abandonment of connectionism,
– 1971−75: DARPA's frustration with the Speech Understanding
Research program at Carnegie Mellon University,
– 1973: the large decrease in AI research in the United Kingdom in
response to the Lighthill report,
– 1973−74: DARPA's cutbacks to academic AI research in general,
– 1987: the collapse of the Lisp machine market,
– 1988: the cancellation of new spending on AI by the Strategic
Computing Initiative,
– 1993: expert systems slowly reaching the bottom,
– 1990s: the quiet disappearance of the fifth-generation computer
project's original goals,
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Artificial Intelligence
Brief History – The Comeback
– Rebirth of Connectionism
• The backpropagation algorithm (Hinton and others, 1986, PDP group)
– Machine learning becomes usable
• The ID3 and C4.5 algorithms – decision trees for the masses - R. Quinlan, 86
• Increased computing power
• Increased availability of data in electronic form
– Behavior-based (or “emerging”) A.I.
• “A robust layered control system for a mobile robot” – R. Brooks, 85
• “Intelligence is in the eye of the beholder”, “The world is its own best model”,
“Elephants don’t play chess”, “We don’t need no representation”
• Agent-based architectures (Maes, and many others)
– Active Vision
• The goal of machine perception is not to build a 3D model of the world, but to
extract information to perform useful tasks (D. Ballard, Y. Aloimonos)
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Artificial Intelligence
Brief History – Present Times
–Realistic expectations
–Lots of useful applications
–Research divided into subareas
(vision, learning, NLP, planning,
etc.)
–Little work on overall
intelligence
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Artificial Intelligence
The Old Times
The pursuit of “General AI”
Objective: Build a machine that exhibits ALL
of the AI features
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Old Times – The Turing Test
How do we know when AI research has
succeed?
When a program that can consistently pass the
Turing test is written.
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Old Times – The Turing Test
A human judge engages in a natural
language conversation with one
human and one machine, each of
which tries to appear human; if the
judge cannot reliably tell which is
which, then the machine is said to
pass the test.
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Old Times – The Turing Test
Problems with the Turing test:
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Old Times – The Turing Test
Problems with the Turing test:
• Human intelligence vs. general intelligence
– Computer is expected to exhibit undesirable
human behaviors
– Computer may fail for being too smart
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Old Times – The Turing Test
Problems with the Turing test:
• Human intelligence vs. general intelligence
– Computer is expected to exhibit undesirable
human behaviors
– Computer may fail for being too smart
• Real intelligence vs. simulated intelligence
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Old Times – The Turing Test
Problems with the Turing test:
• Human intelligence vs. general intelligence
– Computer is expected to exhibit undesirable
human behaviors
– Computer may fail for being too smart
• Real intelligence vs. simulated intelligence
• Do we really need a machine that passes it?
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Old Times – The Turing Test
Problems with the Turing test:
• Human intelligence vs. general intelligence
– Computer is expected to exhibit undesirable
human behaviors
– Computer may fail for being too smart
• Real intelligence vs. simulated intelligence
• Do we really need a machine that passes it?
• Testing the machine vs. testing the judge
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Old Times – The Turing Test
Problems with the Turing test:
• Human intelligence vs. general intelligence
– Computer is expected to exhibit undesirable
human behaviors
– Computer may fail for being too smart
•
•
•
•
Real intelligence vs. simulated intelligence
Do we really need a machine that passes it?
Testing the machine vs. testing the judge
Too hard! – Very useful applications can be
built that don’t pass the Turing test
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More Recent Research
Goal: Build “intelligent” programs that are useful for a
particular task
Normally restricted to one target intelligent behavior.
Thus AI has been broken into several sub-areas:
– Machine learning
– Robotics
– Computer vision
– Natural language processing
– Knowledge representation and reasoning
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What has AI done for us?
State of the Art
It has provided computers that are able to:
• Learn (some simple concepts and tasks)
• Allow robots to navigate autonomously (in
simplified environments)
• Understand images (of restricted predefined types)
• Understand human languages (some of them,
mostly written, with limited vocabularies)
• Reason (using brute force, in very restricted
domains)
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What has AI done for us?
Machine Learning – Netflix movie recommender system
Very active research area
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Extract statistical regularities from data
Find decision boundaries
Find decision rules
Imitate human brain
Imitate biological evolution
Combine several approaches
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What has AI done for us?
Machine Learning – Netflix movie recommender system
Idea:
• After returning a movie, user assigns a grade to it
(from 1 to 5)
• Given (millions) of records of users, movies and
grades, and the pattern of grades assigned by the
user, the system presents a list of movies the user
is likely to grade highly
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What has AI done for us?
Robotics - Stanley, a self-driving car
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What has AI done for us?
Robotics - Stanley, a self-driving car
What does Stanley learn?
A mapping from sensory inputs to driving commands
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What has AI done for us?
Robotics - Lexus self-parking system
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What has AI done for us?
Computer Vision - Face Detecting
Cameras
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What has AI done for us?
Computer Vision - Face
Detecting Cameras
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What has AI done for us?
Reasoning
Successful applications:
• Route planning systems
• Game playing programs
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What has AI done for us?
Reasoning
The Zohirushi Neuro Fuzzy® Rice Cooker & Warmer features advanced Neuro
Fuzzy® logic technology, which allows the rice cooker to 'think' for itself and
make fine adjustments to temperature and heating time to cook perfect rice
every time.
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What has AI done for us?
Natural language processing
Successful applications:
• Dictation systems
• Text-to-speech systems
• Text classification
• Automated summarization
• Automated translation
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What has AI done for us?
Natural language processing
Automated Translation
Original English Text:
The Dodgers became the fifth team in modern major league
history to win a game in which they didn't get a hit,
defeating the Angels 1-0. Weaver's error on a slow roller led
to an unearned run by the Dodgers in the fifth.
What has AI done for us?
Natural language processing
Automated Translation
Original English Text:
The Dodgers became the fifth team in modern major league
history to win a game in which they didn't get a hit,
defeating the Angels 1-0. Weaver's error on a slow roller led
to an unearned run by the Dodgers in the fifth.
Translation to Spanish (by Google - 2009)
Los Dodgers se convirtió en el quinto equipo en la moderna
historia de las ligas mayores para ganar un juego en el que
no obtener una respuesta positiva, derrotando a los Ángeles
1-0. Weaver's error en un lento rodillo dado lugar a un
descontados no correr por la Dodgers en el quinto.
What has AI done for us?
Natural language processing
Automated Translation
Translation to Spanish (by Google - 2009)
Los Dodgers se convirtió en el quinto equipo en la moderna
historia de las ligas mayores para ganar un juego en el que
no obtener una respuesta positiva, derrotando a los Ángeles
1-0. Weaver's error en un lento rodillo dado lugar a un
descontados no correr por la Dodgers en el quinto.
What has AI done for us?
Natural language processing
Automated Translation
Translation to Spanish (by Google – 2009)
Los Dodgers se convirtió en el quinto equipo en la moderna
historia de las ligas mayores para ganar un juego en el que
no obtener una respuesta positiva, derrotando a los Ángeles
1-0. Weaver's error en un lento rodillo dado lugar a un
descontados no correr por la Dodgers en el quinto.
Translation back to English (by Google – 2009)
The Dodgers became the fifth equipment in the modern history
of the leagues majors to gain a game in which not to obtain
a positive answer, defeating to Los Angeles 1-0. Weaver' s
error in a slow given rise roller to discounting not to run by
the Dodgers in fifth.
What has AI done for us?
Natural language processing
Automated Translation
Original English Text:
The Dodgers became the fifth team in modern major league
history to win a game in which they didn't get a hit,
defeating the Angels 1-0. Weaver's error on a slow roller led
to an unearned run by the Dodgers in the fifth.
What has AI done for us?
Natural language processing
Automated Translation
Original English Text:
The Dodgers became the fifth team in modern major league
history to win a game in which they didn't get a hit,
defeating the Angels 1-0. Weaver's error on a slow roller led
to an unearned run by the Dodgers in the fifth.
Translation to Spanish (by Google - 2010)
Los Dodgers se convirtió en el quinto equipo en la historia
moderna de Grandes Ligas en ganar un juego en el que no
recibieron una respuesta positiva, derrotando a los
Angelinos 1-0. error de Weaver en una rola lenta dio lugar a
una carrera sucia por los Dodgers en el quinto.
What has AI done for us?
Natural language processing
Automated Translation
Translation to Spanish (by Google – 2010)
Los Dodgers se convirtió en el quinto equipo en la historia
moderna de Grandes Ligas en ganar un juego en el que no
recibieron una respuesta positiva, derrotando a los
Angelinos 1-0. error de Weaver en una rola lenta dio lugar a
una carrera sucia por los Dodgers en el quinto.
Translation back to English (by Google – 2010)
The Dodgers became the fifth side in the modern history of
baseball to win a game that did not get a hit, defeating the
Angels 1-0. Weaver's error on a slow roller led to an
unearned run for the Dodgers in the fifth.
The Future of AI
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The Future of AI
Making predictions is hard, especially about the future - Yogi
Berra
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The Future of AI
Making predictions is hard, especially about the future - Yogi
Berra
But…
• Continued progress expected
• Greater complexity and autonomy
• New enabling technology - Metalearning
• Once human-level intelligence is attained, it will be quickly
surpassed
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Conclusions
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Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
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Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
• The goal of general AI has been abandoned (at least
temporarily)
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Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
• The goal of general AI has been abandoned (at least
temporarily)
• Useful applications have appeared in all subfields of AI,
including: Machine learning, computer vision, robotics,
natural language processing and knowledge representation
47
Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
• The goal of general AI has been abandoned (at least
temporarily)
• Useful applications have appeared in all subfields of AI,
including: Machine learning, computer vision, robotics,
natural language processing and knowledge representation
• The field continues to evolve rapidly
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Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
• The goal of general AI has been abandoned (at least
temporarily)
• Useful applications have appeared in all subfields of AI,
including: Machine learning, computer vision, robotics,
natural language processing and knowledge representation
• The field continues to evolve rapidly
• Increased complexity and unpredictability of AI programs
will raise important ethics issues and concerns
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AI and Psychology
Some questions/issues:
• Can A.I. algorithms be used to model natural
intelligence?
• How can we exploit our knowledge of human
intelligence to develop artificially intelligent
systems?
• Can psychology help settle the Strong A.I. vs.
Weak A.I. debate?
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THANKS!
Questions?
For more info:
http://www.cs.utep.edu/ofuentes/
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UTEP’s Vision and Learning
Laboratory
Our goals:
Programming computers to see
Programming computers to learn
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UTEP’s Vision and Learning
Laboratory
A Sample of Current Research Projects
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Transfer learning using deep neural networks
Image super-resolution
Image compression
Tracking multiple nearly-identical objects
Vision with foveal cameras
Medical image analysis
Astronomical data analysis
Predicting RNA folding
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Machine Learning
Why?
Scientific Reason:
• Learning is arguably the most important feature of
intelligence.
• If we want to understand and replicate intelligent
behavior, the ability to learn is indispensable.
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Machine Learning
Why?
Engineering Reason:
• Programs that allow computers to learn are the
only practical way of solving a wide variety of
very difficult problems.
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
• Traditional Approach
– Write a detailed sequence of instructions (a program)
that tells the computer how to solve the problem.
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
• Traditional Approach
– Write a detailed sequence of instructions (a program)
that tells the computer how to solve the problem.
• Machine Learning Approach
– Give the computer examples of desired results and let it
learn how to solve the problem.
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
• Traditional Approach
– Write a detailed sequence of instructions (a program)
that tells the computer how to solve the problem.
• Machine Learning Approach
– Give the computer examples of desired results and let it
learn how to solve the problem.
– Advantage: It allows to solve problems that we can’t
solve with the traditional approach
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
• Traditional Approach
– Write a detailed sequence of instructions (a program)
that tells the computer how to solve the problem.
• Machine Learning Approach
– Give the computer examples of desired results and let it
learn how to solve the problem.
– Advantage: It allows to solve problems that we can’t
solve with the traditional approach
– Most applications in other AI areas are based on machine
learning
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Background: Artificial Neural
Networks
Idea: Imitate the information processing
capabilities of the central nervous system using a
large set of simple parallel non-linear units
(“artificial neurons”)
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Background: Artificial Neural
Networks
The output of a neuron is given by a combination of its inputs and
its associated weights
We use a set of examples to adjust the weights to allow the
network to give the correct output – this is called training the
network
We use the trained network to solve problems!
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Artificial Neural Networks
Face Recognition
Input: Pixel intensities
Output: Vicente Fox!
Input: Pixel intensities
Output: Angelina Jolie!
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Artificial Neural Networks
Classification of Infant Cry
Input: Sound intensities
over time
Output: Hungry
Input: Sound intensities
over time
Output: Pain
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Artificial Neural Networks
Driving Autonomous Vehicles
Input: Pixel
intensities
Output: Left
Input: Pixel
intensities
Output: Straight
Input: Pixel
intensities
Output: Right
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Background: Artificial Neural
Networks
Advantages:
• Can approximate many types of functions (real-valued,
discrete-valued, vector-valued)
• Relatively insensitive to noise
Disadvantages:
• Training times can be long
• Overfitting –network “memorizes” training data and is
unable to generalize
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Transfer learning using deep
neural networks
Research Question: Can we take advantage of
previously-acquired knowledge in order to learn
to solve a new task more quickly and/or with less
training data?
It works for humans:
- Knowing Spanish helps to learn Portuguese
- A tennis player can easily learn to play squash
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Transfer learning using deep
neural networks
Idea # 1:
• Use a network with many layers
• Train network for a task
• Retrain only the last one or two layers in order to learn a new but
similar task
Idea # 2:
•
•
•
•
Use output layer with many neurons
Assign randomly chosen values to output neurons for each class
Train network for a task
When new class is added to task, find average value in output layer
and assign that as the label for the class
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Transfer learning using deep
neural networks
Idea # 1:
• Use a network with many layers
• Train network for a task
• Retrain only the last one or two layers in order to learn a new but
similar task
Results
• We trained a network to recognize handwritten letters
• We can re-train a network to recognize digits using less than 5% of
the time and training examples
Potential Applications
• Speech recognition
• Face recognition
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Transfer learning using deep
neural networks
Idea # 2:
•
•
•
•
Use output layer with many neurons
Assign randomly chosen values to output neurons for each class
Train network for a task
When new class is added to task, find average value in output layer
and assign that as the label for the class
Results
• We trained a network to recognize handwritten letters
• Network can recognize digits without any retraining
Potential Applications
• Flexible face recognition systems
• Language models with unlimited vocabulary
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Image Super-resolution
Research Question: Can we increase the resolution of an
image of a known class (say faces, or hand-written text)
in software? That is, given a low-resolution (LR) image
of an object, can we infer its appearance in highresolution (HR)?
Idea:
Crate many pairs of (LR,HR) images
Learn function from LR to HR (the opposite is trivial)
When given a LR image, apply learned function to increase
resolution
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Image Super-resolution
LR input image
Generated image
Original HR image
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Image Super-resolution
Theory and Algorithms
 Decouple high-resolution face image to two parts
=
+
I Hg
I Hl
I H — high resolution face image I Hg — global face I Hl — local face
IH
 Two-step Bayesian inference
I H*  arg max p ( I L | I H ) p ( I H )
IH
IH
?
 arg max p ( I L |
I Hg , I Hl
I Hg , I Hl ) p ( I Hg , I Hl )
 arg max p ( I L | I Hg ) p ( I Hg ) p ( I Hl | I Hg )
IL
I Hg , I Hl
1. Inferring global face
1. Inferring
global face
g*
I H  arg max p( I L | I Hg ) p( I Hg )
I Hg *
2. Inferring local face
2. Inferring
local face
I Hl *  arg max
p ( I Hl | I Hg * )
I Hl *
Finally adding them together
Finally
adding them together
I H*  I Hl *  I Hg *
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Image Super-resolution
Our Contributions:
• Improved image alignment: k-nearest-neighbors warping
function
• More efficient reconstruction: a stochastic algorithm to
build local model
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Image Super-resolution
Experimental results
(a)
(b)
(c)
(d)
(e)
(a) LR image Restored full face. (c) Restored global face. (d) Interpolated face. (e) Original face. (f) Restored local face.
(f)
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Compression of Medical Images
using Super-resolution
Observation: if we can derive the HR image from the
LR one, then we don’t need to store the HR image!
Idea:
• Segment image into regions of high, medium and
low importance
• Compress low importance pixels to a single bit
• Compress/decompress medium importance pixels
using super-resolution
• Compress/decompress high importance area using
lossless compression algorithm such as jpeg2000 76
Compression of Medical Images
using Super-resolution
a) Original mammogram; b), c) and d) show different
compression/decompression results for different compression
parameters
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Compression of Medical Images
using Super-resolution
Compression Method PSNR Compression Ratio
JPEG 2000
41.95
80:1
Lossless JPEG
infinite
3:1
Our method
35.90
20480:1
Experimental results
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Tracking Nearly Identical
Objects
Problem:
Given a video with multiple moving objects
that are similar, determine the trajectories of
each individual object
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Tracking Nearly Identical Objects
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Tracking Nearly Identical Objects
Approach:
• Build probabilistic models of motion of objects from
training video
• Find objects on consecutive frames f(i) and f(i+1)
• Use a backtracking search algorithm to find the set
of matches that maximizes probability, given models
Application:
Tracking flies – experiments to determine the effect of
alcohol ingestion on groups of flies
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Tracking Nearly Identical Objects
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Tracking Nearly Identical Objects
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Traffic Sign Inspection and Tracking
Tracking – Locating an object of interest in a sequence of
images, despite variations in size and appearance
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Machine Learning in Science
• Problem: Gathering scientific data is easy, gaining knowledge
from them is not!
– Huge amounts of data gathered with automated devices – Digital sky
surveys, medical databases, particle physics experiments, seismic data
– Data easily available through the Internet
– Not enough scientists to fully exploit data
• Thus, there's a need for automated methods to classify and
analyze the data and to derive knowledge and insight from
them.
• Machine learning offers a very promising methodology to attain
these goals
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Example: Estimation of Stellar
Atmospheric Parameters from Spectra
What's a spectrum?
A plot of energy flux against wavelength. It shows the combination of
black body radiation (originated in the core) and absorption lines
(originated in the atmosphere)
I
86
Estimation of Stellar Atmospheric
Parameters
• Quantum theory tells us that an atom can absorb energy only
at certain discrete wavelengths.
• Thus, we can view an absorption line as the signature of the
atom, ion or molecule that produces it.
• An expert astronomer can estimate the properties of the star’s
atmosphere from the strength of the absorption lines.
• In particular, the effective temperature, surface gravity and
metallicity can be estimated from the strength of absorption
lines.
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Estimation of Stellar Atmospheric
Parameters - Problem: Too many stars!
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Experimental Results
Estimation of Stellar Atmospheric
Parameters
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Experimental Results
Estimation of Stellar Atmospheric
Parameters
Ensemble
LWLR
OC
Error
Reduction
Teff[k]
143.33
126.88
11.5%
Log g[dex]
0.3221
0.2833
12%
Fe/H
0.223
0.172
22.9%
90
Vision with foveal cameras
Mission: transmit faces
28x37
chip
76x150
chip
Before transmission:
•Tune/enhance resolution
•Compress appropriately
46x35 full frame
Sampled from 3600x2700 sensor
91
Challenge – where to foveate?
Problem: Foveation is useful as long as we can effectively select the image regions where
super-resolution should be applied.
Our Approach:
Use motion as a first trigger of higher resolution
Learn from multiple positive and negative examples of objects of the class of interest
(e.g. faces, pedestrians, vehicles)
Challenges:
– The same algorithm must be applicable to widely varying object classes
– Choice of features
– Choice of (negative) examples
– Combination of detectors
Results:
•
We have built systems that detect over 90% of the faces in images, while yielding
a false-positive rate of about 0.00001%
•
Our methods can easily be implemented in systolic hardware
92
Foveating on faces
93
Foveating on faces
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Motion-triggered Foveation
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THANKS!
For more info:
http://www.cs.utep.edu/ofuentes/
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Acknowledgements
This work was actually done by:
• Steven Gutstein
• Jason Zheng
• Geovany Ramirez
• Diego Aguirre
• Joel Quintana
• Peter Kelley
• Manali Chakraborty
• Trilce Estrada
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