Image object labelling and classification using an associative memory

S. E M O'Keefe*, J. Austin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An essential part of image analysis is the location and identification of objects within the image. Noise and clutter make this identification problematic, and the size of the image may present a computational problem. To overcome these problems, we use a window onto the image to focus onto small areas. Conventionally we still need to know the size of the object we are searching for in order to select a window of the correct size. We describe a method for object location and classification which enables us to use a small window to identify large objects in the image. The window focusses on features in the image, and an associative memory recalls evidence for objects from these features, avoiding the necessity of knowing the dimensions of the objects to be detected.

Original languageEnglish
Title of host publicationFifth International Conference on Image Processing and its Applications, 1995
Place of PublicationLondon
PublisherIET
Pages286-290
Number of pages5
ISBN (Print)0-85296-642-3
DOIs
Publication statusPublished - 1995
EventFifth International Conference on Image Processing and its Applications - Edinburgh, United Kingdom
Duration: 4 Jul 19956 Jul 1995

Conference

ConferenceFifth International Conference on Image Processing and its Applications
Country/TerritoryUnited Kingdom
CityEdinburgh
Period4/07/956/07/95

Bibliographical note

© IEE 1995. This paper is a postprint of a paper submitted to and accepted for publication in the Proceedings of the Fifth International Conference on Image Processing and its Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.

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