Logo Recognition Using Geometric Invariants
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Logo recognition using geometric invariants.
In Document Analysis and Recognition, 894--897, 1993
Online Version
A pdf version is available for download.
Abstract
The problem of logo recognition is of great interest in the document domain, especially for databases, because of its potential for identifying the source of the document and its generality as a recognition problem. By recognizing the logo, one obtains semantic information about the document, which may be useful in deciding whether or not to analyze the textual components. A multi-level stages approach to logo recognition which uses global invariants to prune the database and local affine invariants to obtain a more refined match is presented. An invariant signature which can be used for matching under a variety of transformations is obtained. The authors provide a method of computing Euclidean invariants and show how to extend them to capture similarity, affine, and projective invariants when necessary. They implement feature detection, feature extraction, and local invariant algorithms and successfully demonstrate the approach on a small database
Co-authors
Bibtex Entry
@inproceedings{DoermannRW93i,
title = {Logo recognition using geometric invariants},
author = {David Doermann and Ehud Rivlin and Isaac Weiss},
year = {1993},
booktitle = {Document Analysis and Recognition},
pages = {894--897},
abstract = {The problem of logo recognition is of great interest in the document domain, especially for databases, because of its potential for identifying the source of the document and its generality as a recognition problem. By recognizing the logo, one obtains semantic information about the document, which may be useful in deciding whether or not to analyze the textual components. A multi-level stages approach to logo recognition which uses global invariants to prune the database and local affine invariants to obtain a more refined match is presented. An invariant signature which can be used for matching under a variety of transformations is obtained. The authors provide a method of computing Euclidean invariants and show how to extend them to capture similarity, affine, and projective invariants when necessary. They implement feature detection, feature extraction, and local invariant algorithms and successfully demonstrate the approach on a small database}
}