O'Keefe, S. E M orcid.org/0000-0001-5957-2474 and Austin, J. orcid.org/0000-0001-5762-8614 (1995) Image object labelling and classification using an associative memory. In: Fifth International Conference on Image Processing and its Applications, 1995. Fifth International Conference on Image Processing and its Applications, 04-06 Jul 1995 IET , GBR , pp. 286-290.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 23 Apr 2014 14:00 |
Last Modified: | 21 Jan 2025 18:21 |
Published Version: | https://doi.org/10.1049/cp:19950666 |
Status: | Published |
Publisher: | IET |
Identification Number: | 10.1049/cp:19950666 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75082 |