Computer Vision
Ronald Frazier
CIS 479
April 20, 1999
Introduction
• Important area of study
– Ease of use for new users
– Managing large quantities of images
Combines a Variety of
Disciplines
• Standard Programming Techniques
• Artificial Intelligence Techniques
– Neural Networks
– Fuzzy Logic
• Image Processing Techniques
• Human Biology and Physiology
Optical Character Recognition
(OCR)
• Converting graphical representation of
text to character based representation
• One of the most developed areas of
computer vision
• Lots of ideas for further research
OCR - Character Recognition
• Examining graphical representation of
character to determine character
represented
• Identification Process
– Apply image processing
– Identify character (usually using neural
networks)
OCR - Sample Character
Recognition Algorithm
• Chaincode Algorithm
– Apply image processing to get outline
– Break character into 4x4 grid
– Calculate average slope and curvature for
each cell
OCR - Sample Character
Recognition Algorithm
• Chaincode Algorithm
– Data processed by Neural Network
– Character recognized by Neural Network
Types of OCR
• Preprinted Text
• Live Handwriting
OCR - Preprinted Text
• Text that has already been written or
typed before recognition begins
– ex: Encyclopedia, Contract, Report
• Can be used on printed or handwritten
documents
OCR - Preprinted Text
• Recognition Process
– Identify possible individual characters
• Standard character recognition techniques
– determine possible words
– Compare to dictionary and determine
existing words
– Select most likely existing word
Applications - U.S. Postal
Service
• Handle over 100 billion parcels yearly.
• Need automated way to identify
destination of each parcel and print a
barcode for faster processing.
• For more details, see my web site
OCR - Live Handwriting
• Process text as it is written
– ex: Personal Digital Assistants
• Can be used on handwritten documents
• Track time, direction, and angles of lines
as they are written and use it to identify
character
Content Based Image
Recognition (CBIR)
• Identifying contents of images
• Applications
– Automated organization and classification of
images
– Image database searching
– Still images or video
CBIR - Recognition Methods
• Content that can be recognized:
– Specific colors and approximate proportions
• ex: A lot of Red, a little bit of Green
– Objects base on shapes, colors, textures,
edges, size, etc.
– Face detection
Applications - News Video
Recognition and Retrieval
• Store video in database along with
description of content for searching
• Automated determination of video
content
Applications - News Video
Recognition and Retrieval
• Processing Technique
– locate and extract on screen captions and
closed captions
– Apply OCR to convert captions to text
Applications - News Video
Recognition and Retrieval
• Processing Difficulties
– Interference from background image
• Handled by Frame Filtering and Frame
ANDing
– Low resolution of captions
• Handled by magnifying characters and
interpolating pixel values
Other Possible Uses of
Computer Vision
• Robots
• Self-Controlled Vehicles
• Security - Fingerprint and Retina
Matching
Additional Information
• For more information on additional topics
not covered in this presentation, along
with links to other computer vision
pages, see my web site at:
http://people.mw.mediaone.net/ldkronos/AI