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Local Binary Patterns: New Variants and Applications pp 85–112Cite as

The Geometric Local Textural Patterns (GLTP)

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 506))

Abstract

In this chapter we present a family of techniques based on the principle of the Local Binary Pattern (LBP) technique. This family is called the Geometric Local Textural Patterns (GLTP). Classical LBP techniques are based on exploring intensity changes around each pixel in an image using close neighbourhoods. The main novelty of the GLTP techniques is that they explores intensity changes on oriented neighbourhoods instead of on close neighbourhoods. An oriented neighbourhood describes a particular geometry composed of points on circles with different radii around the center pixel. A digital representation of the points on the oriented neighbourhood defines a GLTP-code. Symmetric versions of the geometries around the pixel are assessed the same GLTP code. Each pixel in the image is assigned a set of GLTP-codes, each for a particular geometry. The texture of an image is characterized with a GLTP histogram of the occurrences of the GLTP-codes on the whole image. We explain the principle of the techniques using the simplest case, called the Geometric Local Binary (GLBP) technique, which is based on boolean comparisons. Then we present variations of this technique to enlarge the family of GLTP techniques. We quantify the texture difference between a pair or images or regions by computing the divergence between their corresponding GLTP-histograms using an adaptation of the Jensen-Shannon entropy.

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Abbreviations

LBP :

Local Binary Pattern

LDP :

Local Derivative Pattern

FLS :

First order Local Sign

AD-LBP :

Angular Difference Local Binary Patterns

RD-LBP :

Radial Difference Local Binary Patterns

GLBP :

Geometric Local Binary Pattern

GLTP :

Geometric Local Textural Pattern

GLtP :

Geometric Local Ternary Pattern

GLDP :

Geometric Local binary, with Derivative features, Pattern

GLCP :

Geometric Local binary, with Complement features, Pattern

GLDCP :

Geometric Local binary, with Derivative and Complement features, Pattern

LBPD :

LBP Derivative

GMM :

Gaussian Mixture Models

EM :

Expectation Maximization algorithm

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Authors and Affiliations

  1. Antonio Nariño University, Cra 10 No. 17-35 Barrio Ancón, Ibagué, Tolima, Colombia

    S. A. Orjuela Vargas & J. P. Yañez Puentes

  2. TELIN-IPI-IMINDS Ghent University, St-Pietersnieuwstraat 41, B-9000 , Gent, Belgium

    W. Philips

Authors
  1. S. A. Orjuela Vargas
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  2. J. P. Yañez Puentes
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  3. W. Philips
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Corresponding author

Correspondence to S. A. Orjuela Vargas .

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Editors and Affiliations

  1. Computer Information Systems, Missouri State University, Springfield, USA

    Sheryl Brahnam

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. Departimento di Elettronica e Informatica (DEI), Università di Padova, Padua, Italy

    Loris Nanni

  4. DEIS University of Bologna, Cesena, Italy

    Alessandra Lumini

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Orjuela Vargas, S.A., Yañez Puentes, J.P., Philips, W. (2014). The Geometric Local Textural Patterns (GLTP) . In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-39289-4_4

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