Search Results for author: John McKay

Found 9 papers, 0 papers with code

Discriminative Sparsity for Sonar ATR

no code implementations1 Jan 2016 John McKay, Raghu Raj, Vishal Monga, Jason Isaacs

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines.

Localized Dictionary design for Geometrically Robust Sonar ATR

no code implementations13 Jan 2016 John McKay, Vishal Monga, Raghu Raj

We develop a new localized block-based dictionary design that can enable geometric, i. e. pose robustness.

Dictionary Learning General Classification +1

Using Frame Theoretic Convolutional Gridding for Robust Synthetic Aperture Sonar Imaging

no code implementations26 Jun 2017 John McKay, Anne Gelb, Vishal Monga, Raghu Raj

Recent progress in synthetic aperture sonar (SAS) technology and processing has led to significant advances in underwater imaging, outperforming previously common approaches in both accuracy and efficiency.

Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification

no code implementations26 Jun 2017 John McKay, Vishal Monga, Raghu G. Raj

Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS).

Anomaly Detection Classification +2

What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR

no code implementations29 Jun 2017 John McKay, Isaac Gerg, Vishal Monga, Raghu Raj

Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors.

Transfer Learning

Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging

no code implementations7 Jul 2017 John McKay, Raghu G. Raj, Vishal Monga

The resulting algorithm, fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while retaining high reconstruction quality.

Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing

no code implementations8 Jan 2018 John McKay, Isaac Gerg, Vishal Monga

There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable.

General Classification

Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

no code implementations13 Sep 2019 Albert Reed, Isaac Gerg, John McKay, Daniel Brown, David Williams, Suren Jayasuriya

Acquisition of Synthetic Aperture Sonar (SAS) datasets is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest.

Generative Adversarial Network Image Generation

Towards Multi-Objective Statistically Fair Federated Learning

no code implementations24 Jan 2022 Ninareh Mehrabi, Cyprien de Lichy, John McKay, Cynthia He, William Campbell

With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients.

Data Poisoning Fairness +1

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