Detecting animals in African Savanna with UAVs and the crowds

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Recent technological advancements in hardware systems have made higher quality cameras. State of the art panoramic systems use them to produce videos with a resolution of 9000 x 2400 pixels at a rate of 30 frames per second (fps).(1) Many modern applicatio ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The Panoptic camera is an omnidirectional multi-aperture visual system which is realized by mounting multiple imaging sensors on a hemispherical frame. In this chapter, we will present a novel distributed and parallel implementation of the real-time omnidi ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

This study highlights the benefit of precise aerial position control in the context of mapping using frame-based imagery taken by small UAVs. We execute several flights with a custom Micro Aerial Vehicle (MAV) octocopter over a small calibration field equi ...

Detecting that pedestrians are present in front of a vehicle is highly desirable to avoid dangerous traffic situations. A novel vision-based system is presented to automatically detect far-away pedestrians with low-resolution cameras mounted in vehicles gi ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

This work presents an embedded solution for detecting and classifying head-level objects using stereo vision to assist blind individuals. A custom dataset was created, featuring five classes of head-level objects, selected based on a survey of visually imp ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Unmanned vehicles have become indispensable tools in various fields such as agriculture, environmental monitoring, and disaster response. A crucial component of these platforms is sensor fusion. As measurements are inherently noisy, certain variables canno ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

The creation of high fidelity synthetic data has long been an important goal in machine learning, particularly in fields like finance where the scarcity of available training and test data make it difficult to utilize many of the deep learning techniques w ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Detecting animals in African Savanna with UAVs and the crowds

Chat with Graph Search

Near-Infrared High-Resolution Real-Time Omnidirectional Imaging Platform for Drone Detection

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Real-Time Omnidirectional Imaging System with Interconnected Network of Cameras

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Mapping with MAV: Experimental Study on the Contribution of Absolute and Relative Aerial Position Control

Robust Real-time Pedestrians Detection in Urban Environments with a Network of Low Resolution Cameras

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback

Fair Decentralized Learning

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Factor-graph Optimisation with Vehicle Dynamic Models for Mapping & Sensing Applications

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Macroeconomic Conditioned Synthetic Financial Markets

Electronic Excited States from Physically Constrained Machine Learning