Search Results for author:
Found 19 papers, 4 papers with code
SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence.
Geometric Wide-Angle Camera Calibration: A Review and Comparative Study
These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools.
UAS Navigation in the Real World Using Visual Observation
Once the agent learns the navigation policy, which means 'familiarized themselves with the environment', we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy.
A Gis Aided Approach for Geolocalizing an Unmanned Aerial System Using Deep Learning
For an unmanned aerial system (UAS), geolocalization ability is an extremely important necessity which is achieved using Inertial Navigation System (INS) with the GPS at its heart.
How important are socioeconomic factors for hurricane performance of power systems? An analysis of disparities through machine learning
This paper investigates whether socioeconomic factors are important for the hurricane performance of the electric power system in Florida.
Learning to Drive Using Sparse Imitation Reinforcement Learning
In this paper, we propose Sparse Imitation Reinforcement Learning (SIRL), a hybrid end-to-end control policy that combines the sparse expert driving knowledge with reinforcement learning (RL) policy for autonomous driving (AD) task in CARLA simulation environment.
Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events
In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes.
Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects
The results of this study characterize the Mahalanobis distances and cosine similarities between DeCAF object manifolds across two visual object tracking benchmark data sets.
Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments.
Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System
This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system.
DeepTracks: Geopositioning Maritime Vehicles in Video Acquired from a Moving Platform
Geopositioning and tracking a moving boat at sea is a very challenging problem, requiring boat detection, matching and estimating its GPS location from imagery with no common features.
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse
In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.
POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound
The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner.
Deep Transformer Networks for Time Series Classification: The NPP Safety Case
An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data and to infer whether a given sequence of events leads to core damage or not.
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes.
Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories
The experiments show that the motion trajectories can be learned by training an recurrent neural network, and temporally consistent geolocation can be predicted with both of the proposed strategies.
UAVid: A Semantic Segmentation Dataset for UAV Imagery
There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the driving car.
MTLE: A Multitask Learning Encoder of Visual Feature Representations for Video and Movie Description
Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data.
4d isip: 4d implicit surface interest point detection
The TSDF represents the distance between the spatial points and object surface points which is an implicit surface representation.
