Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Hand posture recognition using SURF with adaptive boosting

Tools
- Tools
+ Tools

Yao, Yi and Li, Chang-Tsun (2012) Hand posture recognition using SURF with adaptive boosting. In: British Machine Vision Conference (BMVC), Guildford, United Kingdom, 3-7 Sep 2012. Published in: Electronic Proceedings of the British Machine Vision Conference pp. 1-10.

[img]
Preview
PDF
WRAP_Yi_Hand_Posture_2012.pdf - Published Version - Requires a PDF viewer.

Download (435Kb) | Preview

Request Changes to record.

Abstract

An approach making use of SURF feature and Adaboost for hand posture recognition is proposed. First the SURF key points are extracted to describe the blob or ridge-like structures from grey level images. These are potential points of interest that can be used to match with other images with similar structures. Then the statistic parameters of the tendency of gradient changes within small patches surrounding the points of interest are calculated as feature vectors. With all the points of interest, Adaboost is used to train a strong classifier for each posture by selecting the most efficient features, which largely lowers the computational cost of the classification stage. The proposed method was tested on the Triesch Hand Posture Database which is the benchmark in the field. Experimental results showed that our method outperforms existing methods in terms of better recognition accuracy.

Item Type: Conference Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Computer vision
Journal or Publication Title: Electronic Proceedings of the British Machine Vision Conference
Publisher: British Machine Vision Association and Society for Pattern Recognition
Official Date: 2012
Dates:
DateEvent
2012Published
Page Range: pp. 1-10
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Conference Paper Type: Paper
Title of Event: British Machine Vision Conference (BMVC)
Type of Event: Workshop
Location of Event: Guildford, United Kingdom
Date(s) of Event: 3-7 Sep 2012
Related URLs:
  • Organisation
  • Open Access File

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us