Visual object detection by specifying the scale and rotation transformations

Yasuomi D. Sato, Jenia Jitsev, Christoph Von Der Malsburg

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

Abstract

We here propose a simple but highly potential algorithm to detect a model object's position on an input image by determining the initially unknown transformational states of the model object, in particular, size and 2D-rotation. In this algorithm, a single feature is extracted around or at the center of the input image through 2D-Gabor wavelet transformation, in order to find not only the most likely relative size and rotation to the model object, but also the most appropriate positional region on the input image for detecting the correct relative transformational states. We also show the reliable function on the face images of different persons, or of different appearance in the same person.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationModels and Applications - 17th International Conference, ICONIP 2010, Proceedings
Pages616-624
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2010
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 2010 Nov 222010 Nov 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6444 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Neural Information Processing, ICONIP 2010
CountryAustralia
CitySydney, NSW
Period10/11/2210/11/25

Keywords

  • Feature Correspondence
  • Gabor Filter Decomposition
  • Transformation Specific Similarities
  • Visual Object Detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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