A Gabor wavelet pyramid-based object detection algorithm

Yasuomi D. Sato, Jenia Jitsev, Joerg Bornschein, Daniela Pamplona, Christian Keck, Christoph Von Der Malsburg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the correspondingly sub-sampled image smoothed with the low-pass filter. The multi-scale feature correspondence is used for effectively reducing computational costs in filtering. In particular, we show that the multi-scale Gabor feature correspondence play an effective role in matching between an input image and the model representation for object detection.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages232-240
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2011
Event8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China
Duration: 2011 May 292011 Jun 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6676 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Symposium on Neural Networks, ISNN 2011
CountryChina
CityGuilin
Period11/5/2911/6/1

Keywords

  • Computer Vision
  • Gabor Pyramid
  • Multi-scale Feature Correspondence
  • Visual Object Detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A Gabor wavelet pyramid-based object detection algorithm'. Together they form a unique fingerprint.

Cite this