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Found 11 papers, 4 papers with code
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing.
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued.
Sampling Theory for Graph Signals on Product Graphs
In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and recovery of bandlimited graph signals that lie on them.
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.
Signal Processing
Generalized Value Iteration Networks: Life Beyond Lattices
GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.
Fast Resampling of 3D Point Clouds via Graphs
We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.
Signal Representations on Graphs: Tools and Applications
For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.
Image Classification with Rejection using Contextual Information
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information.
A statistical perspective of sampling scores for linear regression
In this paper, we consider a statistical problem of learning a linear model from noisy samples.
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.
Signal Recovery on Graphs: Variation Minimization
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.
