Daniël Reichman

发表

Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining

Jordan M. Malof, Leslie M. Collins, John Bralich, 2017, Defense + Security.

Target localization and signature extraction in GPR data using expectation-maximization and principal component analysis

Leslie M. Collins, Kenneth D. Morton, Peter A. Torrione, 2014, Defense + Security Symposium.

How much shape information is enough, or too much? Designing imaging descriptors for threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

Target signature localization in GPR data by jointly estimating and matching templates

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2015, Defense + Security Symposium.

Improvements to the Histogram of Oriented Gradient (HOG) prescreener for buried threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

The effect of translational variance in training and testing images on supervised buried threat detection algorithms for ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.

Leveraging seed dictionaries to improve dictionary learning

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, 2016 IEEE International Conference on Image Processing (ICIP).

How do we choose the best model? The impact of cross-validation design on model evaluation for buried threat detection in ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

Improving the histogram of oriented gradient feature for threat detection in ground penetrating radar by implementing it as a trainable convolutional neural network

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

Discriminative dictionary learning to learn effective features for detecting buried threats in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

Learning improved pooling regions for the Histogram of Oriented Gradient (HOG) feature for buried threat detection in ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Joseph N. Wilson, 2018, IEEE Transactions on Geoscience and Remote Sensing.

Algorithm development for deeply buried threat detection in GPR data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, SPIE Defense + Security.

On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, IEEE Transactions on Geoscience and Remote Sensing.

If training data appears to be mislabeled, should we relabel it? Improving supervised learning algorithms for threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

How transferable are downward-looking and handheld ground penetrating radar data? Experiments in the context of buried threat detection

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.

An exploration of gradient-based features for buried threat detection using a handheld ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.