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Found 5 papers, 1 papers with code
PhyOT: Physics-informed object tracking in surveillance cameras
Results suggest that our PhyOT can track objects in extreme conditions that the state-of-the-art deep neural networks fail while its performance in general cases does not degrade significantly from that of existing deep learning approaches.
GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP.
The Companion Model -- a Canonical Model in Graph Signal Processing
This paper introduces a $\textit{canonical}$ graph signal model defined by a $\textit{canonical}$ graph and a $\textit{canonical}$ shift, the $\textit{companion}$ graph and the $\textit{companion}$ shift.
Shuffler: A Large Scale Data Management Tool for ML in Computer Vision
Shuffler defines over 40 data handling operations with annotations that are commonly useful in supervised learning applied to computer vision and supports some of the most well-known computer vision datasets.
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
This paper shows that in fact one can develop a unified graph signal sampling theory with analogous interpretations in both domains just like sampling in traditional DSP.
