Improved image classification using morphing

W. Brent Seales, Cheng Jiun Yuan

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


Principal component methods for classifying images have received broad attention and application. For objects with varying appearance, such as three-dimensional objects, increasing the number of object poses represented in the training set is the primary method for improving classification rate. In this paper we show how to improve the performance of this kind of an appearance-based image recognition system. The improvement is obtained by adding new views to the training set which have been generated from existing training data via a morphing algorithm. We show that adding morphed views to the training set increases recognition rate over the same data without morphed views.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 1998 - 3rd Asian Conference on Computer Vision, Proceedings
EditorsRoland Chin, Ting-Chuen Pong
Number of pages8
StatePublished - 1997
Event3rd Asian Conference on Computer Vision, ACCV 1998 - Hong Kong, Hong Kong
Duration: Jan 8 1998Jan 10 1998

Publication series

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


Conference3rd Asian Conference on Computer Vision, ACCV 1998
Country/TerritoryHong Kong
CityHong Kong

Bibliographical note

Publisher Copyright:
© 1997, Springer Verlag. All rights reserved.


  • Appearance models
  • Morphing
  • Object recognition
  • Principal components

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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