Logo image
Back
Stable Bounded Canonical Sets and Image Matching
Book chapter   Peer reviewed

Stable Bounded Canonical Sets and Image Matching

John Novatnack, Trip Denton, Ali Shokoufandeh and Lars Bretzner
Energy Minimization Methods in Computer Vision and Pattern Recognition, pp 316-331
2005
DOI: https://doi.org/10.1007/11585978_21
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Image Match Improve Approximation Algorithm Integer Programming Problem Reference Image Scale Invariant Feature Transform
A common approach to the image matching problem is representing images as sets of features in some feature space followed by establishing correspondences among the features. Previous work by Huttenlocher and Ullman [1] shows how a similarity transformation – rotation, translation, and scaling – between two images may be determined assuming that three corresponding image points are known. While robust, such methods suffer from computational inefficiencies for general feature sets. We describe a method whereby the feature sets may be summarized using the stable bounded canonical set (SBCS), thus allowing the efficient computation of point correspondences between large feature sets. We use a notion of stability to influence the set summarization such that stable image features are preferred.

Metrics

9 Record Views
3 citations in Web of Science
3 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Logo image