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Found 6 papers, 0 papers with code
PatchFlow: Leveraging a Flow-Based Model with Patch Features
Our method illustrates the potential of leveraging computer vision and deep learning techniques to advance inspection capabilities for the die casting industry.
Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation
Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching.
Multi-target Joint Tracking and Classification Using the Trajectory PHD Filter
To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter.
Trajectory PHD Filter with Unknown Detection Profile and Clutter Rate
Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation.
Trajectory PHD and CPHD Filters with Unknown Detection Profile
These filters are referred to as the unknown TPHD (U-TPHD) and unknown TCPHD (U-TCPHD) filters. By minimizing the Kullback-Leibler divergence (KLD), the U-TPHD and U-TCPHD filters can obtain, respectively, the best Poisson and independent identically distributed (IID) density approximations over the augmented sets of trajectories.
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation
Firstly, we extend the concept of JMS to the multi-trajectory scenario of maneuvering target and derive the TPHD recursion for the proposed JMS model.
