research-article

Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images

Authors:

Erik R. Urbach,

Jos B. T. M. Roerdink,

Michael H. F. WilkinsonAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, Issue 2

Pages 272 - 285

https://doi.org/10.1109/TPAMI.2007.28

Published: 01 February 2007 Publication History

Abstract

In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain.

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Cited By

Pyt’ev YChulichkov AFalomkina OBalakin D(2023)Data Analysis and Interpretation: Methods of Computer-Aided Measuring Transducer Theory, Morphological Analysis, Possibility Theory, and Subjective Mathematical ModelingPattern Recognition and Image Analysis10.1134/S105466182304035133:4(1515-1563)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1134/S1054661823040351
Boutry NNajman LGéraud T(2022)Some Equivalence Relation between Persistent Homology and Morphological DynamicsJournal of Mathematical Imaging and Vision10.1007/s10851-022-01104-z64:7(807-824)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10851-022-01104-z
Wilkinson M(2022)Component-Tree Simplification Through Fast Alpha CutsDiscrete Geometry and Mathematical Morphology10.1007/978-3-031-19897-7_19(236-247)Online publication date: 24-Oct-2022
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Index Terms

Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
        Shape representations
  2. Machine learning

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 29, Issue 2

February 2007

176 pages

ISSN:0162-8828

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Copyright © 2007.

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IEEE Computer Society

United States

Publication History

Published: 01 February 2007

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Cited By

Pyt’ev YChulichkov AFalomkina OBalakin D(2023)Data Analysis and Interpretation: Methods of Computer-Aided Measuring Transducer Theory, Morphological Analysis, Possibility Theory, and Subjective Mathematical ModelingPattern Recognition and Image Analysis10.1134/S105466182304035133:4(1515-1563)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1134/S1054661823040351
Boutry NNajman LGéraud T(2022)Some Equivalence Relation between Persistent Homology and Morphological DynamicsJournal of Mathematical Imaging and Vision10.1007/s10851-022-01104-z64:7(807-824)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10851-022-01104-z
Wilkinson M(2022)Component-Tree Simplification Through Fast Alpha CutsDiscrete Geometry and Mathematical Morphology10.1007/978-3-031-19897-7_19(236-247)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19897-7_19
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Alves WHashimoto RMarcotegui B(2017)Ultimate levelingsComputer Vision and Image Understanding10.1016/j.cviu.2017.06.010165:C(60-74)Online publication date: 1-Dec-2017
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