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oai:docta.ucm.es:20.500.14352/194732024-11-26T17:05:32Zcom_20.500.14352_14col_20.500.14352_21

00925njm 22002777a 4500 dc López Molina, Carlos author Montero De Juan, Francisco Javier author Bustince, Humberto author De Baets, Bernard author 2017 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. Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. eds: Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, Cham (2018) 978-3319668260 10.1007/978-3-319-66824-6 38 https://hdl.handle.net/20.500.14352/19473 https//doi.org/10.1007/978-3-319-66824-6 38 https://link.springer.com/chapter/10.1007/978-3-319-66824-6_38 Gradient Fusion Operators for Vector-Valued Image Processing