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Gradient Fusion Operators for Vector-Valued Image Processing

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 642)

Abstract

While classical image processing algorithms were designed for scalar-valued (binary or grayscale) images, new technologies have made it commonplace to work with vector-valued ones. These technologies can involve new types of sensors, as in remote sensing, but also mathematical models leading to an increased cardinality at each pixel. This work analyzes the role of first-order differentiation in vector-valued images; specifically, we explore a novel operator to produce a 2D vector from a Jacobian matrix, in order to represent the variation in a vector-valued image as a planar feature.

Keywords

Vector-valued images Differentiation Jacobian matrix Information fusion 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Dpto. Automatica y ComputacionUniversidad Publica de NavarraPamplonaSpain
  2. 2.Faculty of MathematicsComplutense University and Geosciences Institute IGEO (CSIC-UCM)MadridSpain
  3. 3.KERMIT, Department of Mathematical Modelling, Statistics and BioinformaticsGhent UniversityGhentBelgium

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