Related papers: A real-time UAS hyperspectral anomaly detection sy…
Hyperspectral imaging is useful for applications ranging from medical diagnostics to agricultural crop monitoring; however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption. Snapshot…
Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and…
This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly Detection through Instance Modeling) method for anomaly detection in images, fitting a single multivariate Gaussian (MVG) distribution to the feature vectors…
A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much research has focused on algorithmic improvements on individual…
Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representation-based approaches extract normal image features with a…
The hyperspectral unmixing method is an algorithm that extracts material (usually called endmember) data from hyperspectral data cube pixels along with their abundances. Due to a lower spatial resolution of hyperspectral sensors data in…
Global Navigation Satellite System (GNSS) receivers provide ubiquitous and precise position, navigation, and time (PNT) to a wide gamut of civilian and tactical infrastructures and devices. Due to the low GNSS received signal power, even…
This article proposes a visual inertial navigation algorithm intended to diminish the horizontal position drift experienced by autonomous fixed wing UAVs (Unmanned Air Vehicles) in the absence of GNSS (Global Navigation Satellite System)…
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim…
Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could…
Hyperspectral imaging captures both spectral and spatial information from a sample. The near infrared (NIR, > 800 nm) is advantageous for biomedical imaging as it falls into the tissue transparency window but also contains vibrational…
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often…
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or…
Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to…
Dealing with atypical traffic scenarios remains a challenging task in autonomous driving. However, most anomaly detection approaches cannot be trained on raw sensor data but require exposure to outlier data and powerful semantic…
Autonomous aerial robots are increasingly being deployed in real-world scenarios, where transparent obstacles present significant challenges to reliable navigation and mapping. These materials pose a unique problem for traditional…
Abnormal event detection or anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. Due to the lack of anomalous events at training time, anomaly detection requires the design of…
On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources…
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance and urban traffic monitoring. In the case of urban traffic data, anomalies refer…
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic. Amongst the hardest tasks in medical imaging,…