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Recent years have brought a quick decay in prize of hyperspectral imagery equipment. As a consequence, new applications have appeared, a relevant example being the analysis of agro-food materials. Such applications need to be grounded on dedicated image processing operators, which fully accomplish with (and exploit) the characteristics of hyperspectral imagery. In this regard, we study the quantitative comparison of spectra, which can be further used to produce a variety of image processing operators. Specifically, we propose the use of Baddeley’s Delta metric for the comparison of spectra. Our method has theoretical advantages over classical bandwise comparison measures, which are often inconsistent with human perception of dissimilarity between spectra. Our proposal is put to the test in the context of local contrast computation, with application to item segmentation of in-laboratory imagery.

In this work, using the identification between implication operators and aggregation functions, we study the implication operators that are recovered from overlap functions. In particular, we focus in which properties of implication operators are preserved. We also study how negations can be defined in terms of overlap functions.

AbstractIn this work, using the identification between implication operators and aggregation functions, we study the implication operators that are recovered from overlap functions. In particular, we focus in which properties of implication operators are preserved. We also study how negations can be defined in terms of overlap functions.

AbstractThere exists a large number of techniques for content-aware smoothing. Despite its simplicity, the Perona-Malik Anisotropic Diffusion method is among the most employed ones. In this work we study this method in detail and propose a generalization of its diffusion scheme using restricted dissimilarity functions to measure the intensity differences between neighbouring pixels. This generalization permits a better adaptation of the diffusion process to the characteristics of the images.

There exists a large number of techniques for content-aware smoothing. Despite its simplicity, the Perona-Malik Anisotropic Diffusion method is among the most employed ones. In this work we study this method in detail and propose a generalization of its diffusion scheme using restricted dissimilarity functions to measure the intensity differences between neighbouring pixels. This generalization permits a better adaptation of the diffusion process to the characteristics of the images.

Despite the fact that edges are a low-level feature, the edge detection process is likely to consist of several subprocesses or phases. The one taking the most attention from the research community is the feature extraction or computation, which consists of searching for hints of the presence of an edge at each pixel. In this work we propose an extension of the upper–lower edge detector by changing the way in which it produces the estimation of the presence of an edge. More specifically, we propose to obtain estimations of the intensity change in several orientations and then to combine them using ordered weighting aggregation operators.Despite the fact that edges are a low-level feature, the edge detection process is likely to consist of several subprocesses or phases. The one taking the most attention from the research community is the feature extraction or computation, which consists of searching for hints of the presence of an edge at each pixel. In this work we propose an extension of the upper–lower edge detector by changing the way in which it produces the estimation of the presence of an edge. More specifically, we propose to obtain estimations of the intensity change in several orientations and then to combine them using ordered weighting aggregation operators.