Corner-based feature for object recognition

Jian Cao*, Kan Li, Chunxiao Gao, Qiongxin Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Feature extraction plays a very important role in object recognition and categorization. In this paper, we present a method for extracting corner-based feature from images. This feature is invariant to image scale and rotation, and is shown robust to addition of noise and changes in 3D viewpoint. This paper also describes an approach to using the feature for object recognition. As baselines for comparison, we implemented three additional recognition systems using signature, moment invariant and Fourier descriptor as features. They provide a good basis for judging the importance of representation in learning. The performance analysis on the obtained experimental results demonstrates that the proposed method is effective and efficient.

Original languageEnglish
Title of host publication2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009
Pages106-109
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009 - Wuhan, China
Duration: 30 Nov 20091 Dec 2009

Publication series

Name2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009
Volume3

Conference

Conference2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009
Country/TerritoryChina
CityWuhan
Period30/11/091/12/09

Keywords

  • Clustering
  • Corner
  • Descriptor
  • Feature extraction
  • Object recognition

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