Peter Torrione

Affiliations:
  • Duke University, Durham, NC, USA


According to our database1, Peter Torrione authored at least 25 papers between 2004 and 2017.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2017
A Comparison of Feature Representations for Explosive Threat Detection in Ground Penetrating Radar Data.
IEEE Trans. Geosci. Remote. Sens., 2017

Viewpoint Adaptation for Rigid Object Detection.
CoRR, 2017

2016
A Probabilistic Model for Designing Multimodality Landmine Detection Systems to Improve Rates of Advance.
IEEE Trans. Geosci. Remote. Sens., 2016

2015
Multiple-Instance Hidden Markov Model for GPR-Based Landmine Detection.
IEEE Trans. Geosci. Remote. Sens., 2015

A Nonparametric Bayesian Approach to Multiple Instance Learning.
Int. J. Pattern Recognit. Artif. Intell., 2015

2014
Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data.
IEEE Trans. Geosci. Remote. Sens., 2014

Bayesian Context-Dependent Learning for Anomaly Classification in Hyperspectral Imagery.
IEEE Trans. Geosci. Remote. Sens., 2014

An Open Source Pattern Recognition Toolbox for MATLAB.
CoRR, 2014

2013
Target Classification and Identification Using Sparse Model Representations of Frequency-Domain Electromagnetic Induction Sensor Data.
IEEE Trans. Geosci. Remote. Sens., 2013

Rapid tracking for autonomous driving with monocular video.
Proceedings of the International Conference on Connected Vehicles and Expo, 2013

2012
Histogram of gradient features for buried threat detection in ground penetrating radar data.
Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012

2011
Variational Bayesian Learning for Mixture Autoregressive Models With Uncertain-Order.
IEEE Trans. Signal Process., 2011

Exploiting Ground-Penetrating Radar Phenomenology in a Context-Dependent Framework for Landmine Detection and Discrimination.
IEEE Trans. Geosci. Remote. Sens., 2011

Random set framework for multiple instance learning.
Inf. Sci., 2011

A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification.
Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2011

A hidden Markov context model for GPR-based landmine detection incorporating stick-breaking priors.
Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011

2010
Dirichlet process based context learning for mine detection in hyperspectral imagery.
Proceedings of the 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010

Spatial latency reduction in GPR processing using stochastic sampling.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2010

Context-dependent landmine detection with ground-penetrating radar using a Hidden Markov Context Model.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2010

2009
Multiple instance and context dependent learning in hyperspectral data.
Proceedings of the First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009

2008
Statistical Models for Landmine Detection in Ground Penetrating Radar: Applications to Synthetic Data Generation and Pre-Screening.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2008

2007
Texture Features for Antitank Landmine Detection Using Ground Penetrating Radar.
IEEE Trans. Geosci. Remote. Sens., 2007

2006
Two-dimensional and three-dimensional NUFFT migration method for landmine detection using ground-penetrating Radar.
IEEE Trans. Geosci. Remote. Sens., 2006

Ground Response Tracking for Improved Landmine Detection in Ground Penetrating Radar Data.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2006

2004
Application of texture feature classification methods to landmine/clutter discrimination in off-lane GPR data.
Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, 2004


  Loading...