Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor

Improving the classification of rotated images by adding the signal and magnitude information to... Multimed Tools Appl https://doi.org/10.1007/s11042-018-6204-1 Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor 1 1,2 Raissa Tavares Vieira & Tamiris Trevisan Negri & Adilson Gonzaga Received: 29 August 2017 /Revised: 23 March 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Texture image classification, especially for images with substantial changes in rota- tion, illumination, scale and point of view, is a fundamental and challenging problem in the field of computer vision. Natural images acquired under uncontrolled environments have textures with unknown orientation angles. Therefore, it is difficult to identify the same known texture at different acquisition angles. A common solution is image rotation by means of an interpolation technique. However, texture descriptors are not effective enough when two similar textures acquired at different angles are compared. In this work, we propose a simple and efficient image descriptor, called Completed Local Mapped Pattern (CLMP), and apply it to the texture classification of rotated images. This new approach is an improvement over the previously published Local Mapped Pattern (LMP) descriptor because the new approach includes the signal and the magnitude information. This innovation is more discriminating http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6204-1
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6204-1 Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor 1 1,2 Raissa Tavares Vieira & Tamiris Trevisan Negri & Adilson Gonzaga Received: 29 August 2017 /Revised: 23 March 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Texture image classification, especially for images with substantial changes in rota- tion, illumination, scale and point of view, is a fundamental and challenging problem in the field of computer vision. Natural images acquired under uncontrolled environments have textures with unknown orientation angles. Therefore, it is difficult to identify the same known texture at different acquisition angles. A common solution is image rotation by means of an interpolation technique. However, texture descriptors are not effective enough when two similar textures acquired at different angles are compared. In this work, we propose a simple and efficient image descriptor, called Completed Local Mapped Pattern (CLMP), and apply it to the texture classification of rotated images. This new approach is an improvement over the previously published Local Mapped Pattern (LMP) descriptor because the new approach includes the signal and the magnitude information. This innovation is more discriminating

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 4, 2018

References

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