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Gradient Fusion Operators for Vector-Valued Image Processing

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Lopez-Molina, Carlos and Montero, Javier and Bustince, Humberto and De Baets, Bernard (2017) Gradient Fusion Operators for Vector-Valued Image Processing. In Advances in Fuzzy Logic and Technology. Advances in Intelligent Systems and Computing (642). Springer, pp. 430-442. ISBN 978-3319668260

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Official URL: https://link.springer.com/chapter/10.1007/978-3-319-66824-6_38


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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.


Item Type:Book Section
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International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets Proceedings of the Conference of the European Society for Fuzzy Logic and Technology

Uncontrolled Keywords:Vector-valued images; Differentiation; Jacobian matrix; Information fusion
Subjects:Sciences > Mathematics > Logic, Symbolic and mathematical
ID Code:44908
Deposited On:04 Oct 2017 15:07
Last Modified:04 Oct 2017 15:07

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