Publicaciones
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Neuro-inspired edge feature fusion using Choquet integrals
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
- Marco-Detchart, Cédric
- Lucca, Giancarlo
- Lopez-Molina, Carlos
- De Miguel, Laura
- Pereira Dimuro, Graçaliz
- Bustince, Humberto
[EN] It is known that the human visual system performs a hierarchical information process in
which early vision cues (or primitives) are fused in the visual cortex to compose complex
shapes and descriptors. While different aspects of the process have been extensively stud-
ied, such as lens adaptation or feature detection, some other aspects, such as feature fusion,
have been mostly left aside. In this work, we elaborate on the fusion of early vision prim-
itives using generalizations of the Choquet integral, and novel aggregation operators that
have been extensively studied in recent years. We propose to use generalizations of the
Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection
datasets., The authors gratefully acknowledge the financial support of the Spanish Ministry of Science and Technology (project
PID2019-108392GB-I00 (AEI/10.13039/501100011033), the Research Services of Universidad Pública de Navarra, CNPq
(307781/2016-0, 301618/2019-4), FAPERGS (19/2551-0001660) and PNPD/CAPES (464880/2019-00).
which early vision cues (or primitives) are fused in the visual cortex to compose complex
shapes and descriptors. While different aspects of the process have been extensively stud-
ied, such as lens adaptation or feature detection, some other aspects, such as feature fusion,
have been mostly left aside. In this work, we elaborate on the fusion of early vision prim-
itives using generalizations of the Choquet integral, and novel aggregation operators that
have been extensively studied in recent years. We propose to use generalizations of the
Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection
datasets., The authors gratefully acknowledge the financial support of the Spanish Ministry of Science and Technology (project
PID2019-108392GB-I00 (AEI/10.13039/501100011033), the Research Services of Universidad Pública de Navarra, CNPq
(307781/2016-0, 301618/2019-4), FAPERGS (19/2551-0001660) and PNPD/CAPES (464880/2019-00).
A survey on matching strategies for boundary image comparison and evaluation
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
- Lopez Molina, Carlos
- Marco-Detchart, Cedric
- Bustince, H.
- De Baets, B.
[EN] Most of the strategies for boundary image evaluation involve the comparison of computer-generated images with ground truth solutions. While this can be done in different manners, recent years have seen a dominance of techniques based on the use of confusion matrices. That is, techniques that, at the evaluation stage, interpret boundary detection as a classification problem. These techniques require a correspondence between the boundary pixels in the candidate image and those in the ground truth; that correspondence is further used to create the confusion matrix, from which evaluation statistics can be computed. The correspondence between boundary images faces different challenges, mainly related to the matching of potentially displaced boundaries. Interestingly, boundary image comparison relates to many other fields of study in literature, from object tracking to biometrical identification. In this work, we survey all existing strategies for boundary matching, we propose a taxonomy to embrace them all, and perform a usability-driven quantitative analysis of their behaviour., The authors gratefully acknowledge the financial support of the Spanish Ministry of Science (PID2019-108392GB-I00, AEI/10.13039/50110 0 011033), ass well as that of the Research Foundation Flanders (FWO project 3G.0838.12.N)