Skip to main content
padlock icon - secure page this page is secure

Vision Models Based Identification of Traffic Signs

Buy Article:

$17.00 + tax (Refund Policy)

During the last 10 years, computer hardware technology has been improved rapidly. Large memory, storage is no longer a problem. Therefore some trade-off (dirty and quick algorithms) for traffic sign recognition between accuracy and speed should be improved. In this study, a new approach has been developed for accurate and fast recognition of traffic signs based on human vision models. It applies colour appearance model CIECAM97s to segment traffic signs from the rest of scenes. A Behavioural Model of Vision (BMV) is then utilised to identify the signs after segmented images are converted into grey-level representation. Two standard traffic sign databases are established. One is British traffic signs and the other is Russian traffic signs. Preliminary results show that around 90% signs taken from the British road with various viewing conditions have been correctly identified.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Publication date: January 1, 2002

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Membership Information
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more