Identifying Perceptual Structures In Trademark Images

Victoria Jane Hodge; Garry Phillip Hollier; Jim Austin; John Eakins

Identifying Perceptual Structures In Trademark Images

Victoria Jane Hodge, Garry Phillip Hollier, Jim Austin, John Eakins

Computer Science

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper we focus on identifying image structures at different levels in figurative (trademark) images to allow higher level similarity between images to be inferred. To identify image structures at different levels, it is desirable to be able to achieve multiple views of an image at different scales and then extract perceptually-relevant shapes from the different views. The three aims of this work are: to generate multiple views of each image in a principled manner, to identify structures and shapes at different levels within images and to emulate the Gestalt principles to guide shape finding. The proposed integrated approach is able to meet all three aims.

Original language	English
Pages	1-6
Number of pages	6
Publication status	Published - 13 Feb 2008
Event	Proceedings Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications - Innsbruck, Austria Duration: 13 Feb 2008 → 15 Feb 2008

Conference

Conference	Proceedings Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Country/Territory	Austria
City	Innsbruck
Period	13/02/08 → 15/02/08

Bibliographical note

Keywords

Image segmentation and representation
perceptual s hape finding

Access to Document

HHAE_SPPRA08
© 2008, The Authors. Uploaded with permission of the publisher/copyright holder. Further copying may not be permitted; contact the publisher for details.
Accepted author manuscript, 1.27 MB

Cite this

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title = "Identifying Perceptual Structures In Trademark Images",

abstract = "In this paper we focus on identifying image structures at different levels in figurative (trademark) images to allow higher level similarity between images to be inferred. To identify image structures at different levels, it is desirable to be able to achieve multiple views of an image at different scales and then extract perceptually-relevant shapes from the different views. The three aims of this work are: to generate multiple views of each image in a principled manner, to identify structures and shapes at different levels within images and to emulate the Gestalt principles to guide shape finding. The proposed integrated approach is able to meet all three aims. ",

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AU - Hollier, Garry Phillip

AU - Austin, Jim

AU - Eakins, John

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N2 - In this paper we focus on identifying image structures at different levels in figurative (trademark) images to allow higher level similarity between images to be inferred. To identify image structures at different levels, it is desirable to be able to achieve multiple views of an image at different scales and then extract perceptually-relevant shapes from the different views. The three aims of this work are: to generate multiple views of each image in a principled manner, to identify structures and shapes at different levels within images and to emulate the Gestalt principles to guide shape finding. The proposed integrated approach is able to meet all three aims.

AB - In this paper we focus on identifying image structures at different levels in figurative (trademark) images to allow higher level similarity between images to be inferred. To identify image structures at different levels, it is desirable to be able to achieve multiple views of an image at different scales and then extract perceptually-relevant shapes from the different views. The three aims of this work are: to generate multiple views of each image in a principled manner, to identify structures and shapes at different levels within images and to emulate the Gestalt principles to guide shape finding. The proposed integrated approach is able to meet all three aims.

KW - Image segmentation and representation

KW - perceptual s hape finding

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T2 - Proceedings Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications

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