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

Selecting surface features for accurate multi-camera surface reconstruction

Tools
- Tools
+ Tools

Popham, T. J. and Wilson, Roland (2009) Selecting surface features for accurate multi-camera surface reconstruction. In: British Machine Vision Conference (BMVC), London, UK, 7-10 Sept 2009 doi:10.5244/C.23.107 (Unpublished)

[img]
Preview
PDF
WRAP_Popham_bmvc2009.pdf - Published Version - Requires a PDF viewer.

Download (8Mb) | Preview
[img]
Preview
PDF (Coversheet)
WRAP_Coversheet_FinalVersion (2)_Popham.pdf - Other - Requires a PDF viewer.

Download (94Kb) | Preview
Official URL: http://dx.doi.org/10.5244/C.23.107

Request Changes to record.

Abstract

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector.

Item Type: Conference Item (Poster)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Computer vision, Pattern recognition systems
Official Date: September 2009
Dates:
DateEvent
September 2009Available
DOI: 10.5244/C.23.107
Status: Peer Reviewed
Publication Status: Unpublished
Conference Paper Type: Poster
Title of Event: British Machine Vision Conference (BMVC)
Type of Event: Conference
Location of Event: London, UK
Date(s) of Event: 7-10 Sept 2009
Related URLs:
  • Organisation

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