Presentation 2009-08-31
Object Detection in Images With Cluttered Background by Using Local Features and Their Configuration
Martin KLINKIGT, Koichi KISE, Heiko MAUS, Andreas DENGEL,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) In computer vision object detection is dealing with the problem to find certain objects in an image. Under controlled conditions the results of current systems are reliable. The absence of background clutter is such a condition. Problems arise if these conditions do not hold. In this paper we propose a method for overcoming the problem of background clutter by using a sensitive voting for objects and taking into account the position of local features. In an evaluation our proposed method clearly outperforms a naive object voting, by returning for 64% of the images the correct object compared to 4%.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) PCA-SIFT / Shape context / Hough transform / Implicit shape model / Constellation model
Paper # PRMU2009-65
Date of Issue

Conference Information
Committee PRMU
Conference Date 2009/8/24(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Object Detection in Images With Cluttered Background by Using Local Features and Their Configuration
Sub Title (in English)
Keyword(1) PCA-SIFT
Keyword(2) Shape context
Keyword(3) Hough transform
Keyword(4) Implicit shape model
Keyword(5) Constellation model
1st Author's Name Martin KLINKIGT
1st Author's Affiliation Department of Computer Science and Intelligent Systems, Osaka Prefecture University()
2nd Author's Name Koichi KISE
2nd Author's Affiliation Department of Computer Science and Intelligent Systems, Osaka Prefecture University
3rd Author's Name Heiko MAUS
3rd Author's Affiliation German Research Center for Artificial Intelligence
4th Author's Name Andreas DENGEL
4th Author's Affiliation German Research Center for Artificial Intelligence
Date 2009-08-31
Paper # PRMU2009-65
Volume (vol) vol.109
Number (no) 182
Page pp.pp.-
#Pages 6
Date of Issue