The emerging field of graph signal processing for moving object segmentation
Abstract Moving Object Segmentation (MOS) is an important topic in computer vision. MOS
becomes a challenging problem in the presence of dynamic background and moving …
becomes a challenging problem in the presence of dynamic background and moving …
Graphon pooling for reducing dimensionality of signals and convolutional operators on graphs
A Parada-Mayorga, Z Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this article we propose a pooling approach for convolutional information processing on
graphs relying on the theory of graphons and limits of dense graph sequences. We present …
graphs relying on the theory of graphons and limits of dense graph sequences. We present …
T-HGSP: Hypergraph signal processing using t-product tensor decompositions
Graph signal processing (GSP) techniques are powerful tools that model complex
relationships within large datasets, being now used in a myriad of applications in different …
relationships within large datasets, being now used in a myriad of applications in different …
Sampling and Uniqueness Sets in Graphon Signal Processing
A Parada-Mayorga, A Ribeiro - arXiv preprint arXiv:2401.06279, 2024 - arxiv.org
In this work, we study the properties of sampling sets on families of large graphs by
leveraging the theory of graphons and graph limits. To this end, we extend to graphon …
leveraging the theory of graphons and graph limits. To this end, we extend to graphon …