Search Results for author:
Found 8 papers, 2 papers with code
Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models
There is an increase in interest to model driving maneuver patterns via the automatic unsupervised clustering of naturalistic sequential kinematic driving data.
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples.
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology
Subgraph isomorphism or subgraph matching is generally considered as an NP-complete problem, made more complex in practical applications where the edge weights take real values and are subject to measurement noise and possible anomalies.
Graph-theoretical approach to robust 3D normal extraction of LiDAR data
A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as triangulated irregular network (TIN).
A Continual Learning Framework for Adaptive Defect Classification and Inspection
Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes.
Precision Radiotherapy via Information Integration of Expert Human Knowledge and AI Recommendation to Optimize Clinical Decision Making
The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of $67$ non-small cell lung cancer patients and retrospectively analyzed.
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network
In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans.
WOOD: Wasserstein-based Out-of-Distribution Detection
When part of the test samples are drawn from a distribution that is sufficiently far away from that of the training samples (a. k. a.
