Research TopicsNatural Language Processing

Image Processing

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Natural Language Processing

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What is NLP?

• Natural Language Processing (NLP)– Computers use (analyze, understand,

generate) natural language– A somewhat applied field

• Computational Linguistics (CL)– Computational aspects of the human

language faculty– More theoretical

Why Study NLP?

• Human language interesting & challenging– NLP offers insights into language

• Language is the medium of the web• Interdisciplinary: Ling, CS, psych, math• Help in communication

– With computers (ASR, TTS)– With other humans (MT)

• Ambitious yet practical

Goals of NLP• Scientific Goal

– Identify the computational machinery needed for an agent to exhibit various forms of linguistic behavior

• Engineering Goal– Design, implement, and test systems

that process natural languages for practical applications

Applications• speech processing: get flight information or book a

hotel over the phone• information extraction: discover names of people

and events they participate in, from a document • machine translation: translate a document from

one human language into another• question answering: find answers to natural

language questions in a text collection or database

• summarization: generate a short biography of Noam Chomsky from one or more news articles

General Themes

• Ambiguity of Language• Language as a formal system• Computation with human language• Rule-based vs. Statistical Methods• The need for efficiency

Topic Ideas

1.Text to Speech – artificial voices2.Speech Recognition - understanding3.Textual Analysis – readability4.Plagiarism Detection – candidate selection5.Intelligent Agents – machine interaction

Text to Speech – artificial voice• Text Input• Break text into phonemes

– Match phonemes to voice elements– Concatenate voice elements– Manipulate pitch and spacing

• Output results• Research question: How can a human voice be

used to produce an artificial voice?• Model Talker - opportunities for active, hands-on

research (http://www.modeltalker.com)

Speech Recognition• Spoken Input• Identify words and phonemes in speech

– Generate text for recognized word parts– Concatenate text elements– Perform spelling, grammar and context checking

• Output results• Research question: How can speech recognition

assist a deaf student taking notes in class?• VUST – Villanova University Speech Transcriber

(http://www.csc.villanova.edu/~tway/publications/wayAT08.pdf)

Textual Analysis - Readability

• Text Input• Analyze text & estimate “readability”

– Grade level of writing– Consistency of writing– Appropriateness for certain educ. level

• Output results• Research question: How can computer

analyze text and measure readability?• Opportunities for hands-on research

Plagiarism Detection

• Text Input• Analyze text & locate “candidates”

– Find one or more passages that might be plagiarized– Algorithm tries to do what a teacher does– Search on Internet for candidate matches

• Output results• Research question: What algorithms work like

humans when finding plagiarism?• Experimental CS research

Intelligent Agents

• Example: ELIZA• AIML: Artificial Intelligence Modeling Lang.• Human types something• Computer parses, “understands”, and generates

response• Response is viewed by human• Research question: How can computers

“understand” and “generate” human writing?• Also good area for experimentation

Image Processing

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Some slides from Xin Li lecture notes, West Virginia Univ.

What is Image Processing?

• Digital Image Processing– Analog transmission in 1920– Early improvements in 1920s– Required digital computer (1948)– Rapid advancement since

Historical Background

Newspaper industry used Bartlane cable picture transmission system to send pictures by submarine cable between London and New York in 1920s

The number of distinct gray levels coded by Bartlane system was improved from 5 to 15 by the end of 1920s

Digital Image Processing

• The images in previous slides are digital (now), but they are NOT the result of DIP

• Digital Image Processing is– Processing digital images by a digital

computer• DIP requires a digital computer and other

supporting technologies (e.g., data storage, display and transmission)

Cool Applications

The first picture of moon by US spacecraft Ranger 7on July 31, 1964 at 9:09AM EDT

•Digitization•Compression•Error Recovery

Sir Godfrey N. Housefield and Prof. Allan M. Cormack shared 1979 Nobel Prize in Medicine for the invention of CT

• Enhancement• Edges, Contrast,

Brightness, etc.

• Acquisition – Digital cameras, scanners – MRI and Ultrasound imaging– Infrared and microwave imaging

• Transmission– Internet, wireless communication

• Display– Printers, LCD monitor, digital TV

Past 20 Years

Photography

Motion Pictures

Law Enhancement and Biometrics

Remote Sensing

Hurricane Andrewtaken by NOAA GEOS

America at night (Nov. 27, 2000)

Thermal Images

Human body dispersesheat (red pixels)

Different colors indicatevarying temperatures

Operate in infrared frequency

Medical Diagnostics

chest head

Operate in X-ray frequency

PET and Astronomy

Positron Emission Tomography

Cygnus Loop in theconstellation of Cygnus

Operate in gamma-ray frequency

Cartoon Pictures (Non-photorealistic)

Synthetic Images in Gaming

Age of Empire III by Ensemble Studios

Virtual Reality (Photorealistic)

General Themes

• Human vision is limited• Digital images contain more information

that humans perceive• Computers can use algorithms to extract

more information from digital images• Computers can acquire, manipulate,

compress, transmit and modify images

Topic Ideas1.Biometrics – identifying faces & retinas2.Target Acquisition – see a tank from space3.Computer Vision – detect microscopic flaws in

manufacturing4.Assistive Technology – convert visual images

into tactile or textual form5.Entertainment – remove red eye, morph faces,

digital filmmaking, movie magic6. Image Description – use 3D dictionary to

describe contents of 2D image