Feature Harvesting for Tracking-by-Detection

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Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...

Measuring neural oscillatory synchrony facilitates our understanding of complex brain networks and the underlying pathological states. Altering the cross-regional synchrony-as a measure of brain network connectivity-via phase-locked deep brain stimulation ...

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vis ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composit ...

Using robotics to automate complex manipulation tasks such as cooking could offer significant advantages. Robotics could offer improved throughput, provide assistance for those that lack the required mobility or allow for creative robotic chefs. Generating ...

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epileps ...

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

We present an image-based pipeline for generating geometrical digital twins (GDTs) of stone masonry elements with detail down to the stone level. For this purpose, we acquire RGB images of the individual stones and of the wall during the construction phase ...

A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Plat ...

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

Feature Harvesting for Tracking-by-Detection

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ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

A 16-Channel 60µW Neural Synchrony Processor for Multi-Mode Phase-Locked Neurostimulation

Deep Generative Models and Applications

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Improving the Training of Compact Neural Networks for Visual Recognition

Cognitive workload monitoring in virtual reality based rescue missions with drones

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Joint Human Pose Estimation and Stereo 3D Localization

Complexity and Similarity Metrics for Unsupervised Identification of Programming Methods for Robot Cooking Tasks

Classification of fall directions via wearable motion sensors

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Learning and leveraging shared domain semantics to counteract visual domain shifts

Image-based geometric digital twinning for stone masonry elements

Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars