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A deep active learning framework for crack detection in digital images of paintings

Paintings deteriorate over time due to aging and storage conditions, with cracks being a common form of degradation. Detecting and mapping these cracks is crucial for art analysis and restoration but it presents challenges. Traditional methods often …

Development and validation of fully automated measurement framework to quantify 3D foot and ankle alignment using weight-bearing CT

Introduction/Purpose: Traditionally, foot and ankle alignment is quantified on 2D standing radiographs. This imagingmethod present limitations in capturing the 3D nature of our foot and ankle alignment and the obtained manualmeasurements are prone to …

Recovering from Catastrophic Receptive Field Overflow in Semantic Segmentation of High Resolution Images: Application to Seabed Characterization

This paper addresses a critical issue in seabed characterization with deep learning semantic segmentation using high-resolution Synthetic Aperture Sonar (SAS) data, that we call Catastrophic Receptive Field Overflow (CRFO). We propose novel methods, …

Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images

A new deep learning neural network architecture for seafloor characterisation

Counterfactual functional connectomes for neurological classifier selection

Functional connectivity expresses the correlation of brain activity between regions and helps in understanding and diagnosing neurological conditions and disorders. It also provides discriminative features for machine learning classifiers. We propose …

D4SC : Deep supervised semantic segmentation for seabed characterisation in low label regime

Exact and Heuristic Methods for Simultaneous Sparse Coding

Model-Aware Deep Learning for the Clustering of Hyperspectral Images with Context Preservation

Deep subspace clustering is an effective method for clustering high-dimensional data, and it provides state-of-the-art results in clustering hyperspectral images(HSI). However, these methods typically suffer from the size of the so-called …

Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis