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.
|Title of host publication||Computer Vision - ACCV 1998 - 3rd Asian Conference on Computer Vision, Proceedings|
|Editors||Roland Chin, Ting-Chuen Pong|
|Number of pages||8|
|State||Published - 1997|
|Event||3rd Asian Conference on Computer Vision, ACCV 1998 - Hong Kong, Hong Kong|
Duration: Jan 8 1998 → Jan 10 1998
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||3rd Asian Conference on Computer Vision, ACCV 1998|
|Period||1/8/98 → 1/10/98|
Bibliographical notePublisher Copyright:
© 1997, Springer Verlag. All rights reserved.
Copyright 2017 Elsevier B.V., All rights reserved.
- Appearance models
- Object recognition
- Principal components
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)