Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Exploiting Part-Based Models and Edge Boundaries for Object Detection
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2008 (English)In: Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, 199-206 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores how to exploit shape information to perform object class recognition. We use a sparse partbased model to describe object categories defined by shape. The sparseness allows the relative spatial relationship between parts to be described simply. It is possible, with this model, to highlight potential locations of the object and its parts in novel images. Subsequently these areas are examined by a more flexible shape model that measures if the image data provides evidence of the existence of boundary/connecting curves between connected hypothesized parts. From these measurements it is possible to construct a very simple cost function which indicates the presence or absence of the object class. The part-based model is designed to decouple variations due to affine warps and other forms of shape deformations. The latter are modeled probabilistically using conditional probability distributions which describe the linear dependencies between the location of a part and a subset of the other parts. These conditional distributions can then be exploited to search efficiently for the instances of the part model in novel images. Results are reported on experiments performed on the ETHZ shape classes database that features heavily cluttered images and large variations in scale.

Place, publisher, year, edition, pages
2008. 199-206 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-62474DOI: 10.1109/DICTA.2008.88Scopus ID: 2-s2.0-67549123312ISBN: 978-076953456-5 (print)OAI: oai:DiVA.org:kth-62474DiVA: diva2:480411
Conference
Digital Image Computing: Techniques and Applications, DICTA 2008. Canberra, ACT. 1 December 2008 - 3 December 2008
Note
QC 20120120Available from: 2012-01-19 Created: 2012-01-19 Last updated: 2012-01-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Sullivan, JosephineDanielsson, OscarCarlsson, Stefan
By organisation
Computer Vision and Active Perception, CVAP
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf