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Found 10 papers, 2 papers with code
Histogram Layers for Synthetic Aperture Sonar Imagery
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation.
Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation
The chosen features and resulting segmentation from the image will be assessed based on a select quantitative clustering validity criterion and the subset of the features that reach a desired threshold will be used for the segmentation process.
The Weakly-Labeled Rand Index
In this paper, a labeling approach and associated modified version of the Rand index for weakly-labeled data is introduced to address these issues.
Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery
In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602. 04938) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data.
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
In order to better represent statistical texture information for remote-sensing image classification, in this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network.
Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks
While most deep learning architectures are built on convolution, alternative foundations like morphology are being explored for purposes like interpretability and its connection to the analysis and processing of geometric structures.
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks
Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection.
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures.
Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation
Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor.
