Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited...

A multispectral bayesian classification method for increased acoustic discrimination of seabed sediments using multi-frequency multibeam backscatter data

Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi...

Jump Markov Nonlinear System Identification in Multi-Sensor Target Tracking: A Novel Approach for Multiple Model Joint Tracking and Behavior Classification

The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent...

Outlier detection in multivariate time series: Exploiting reconstructions from random projections

Planetary-scale surface water detection from space

This thesis studies automated methods of surface water detection from satellite imagery. Multiple existing methods are explored, discussed, and some new algorithms are introduced to allowvery accurate detection of surface water and surfacewater changes. Themethods range in applicability from the local level to global, and from detecting high...

Quantitative comparison of cities: Distribution of street and building types based on density and centrality measures

It has been argued that different urban configurations-planned vs. organic, treelike vs. grid like-perform differently when it comes to the intensity and distribution of pedestrian flows, built density and land uses. However, definitions of urban configurations are often rather abstract, ill-defined and at worse end in fixed stereotypes...

Reinforcement Learning of Visual Features

The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they adjust to show us personalized advertisements. These smart programs observe people and serve (other) people. A robot can be seen as a program with a body. Make the program smart enough and it...

Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In...

Unsupervised classification of evolving metropolitan street patterns

Unsupervised classification of evolving metropolitan street patterns

Dominant object detection for autonomous vision-based surveillance

The deployment of visual surveillance and monitoring systems has reached massive proportions. Consequently, a need to automate the processes involved in retrieving useful information from surveillance videos, such as detecting and counting objects, and interpreting their individual and joint behavior, has emerged. The existing methods targeting...

Content Discovery from Composite Audio: An unsupervised approach

In this thesis, we developed and assessed a novel robust and unsupervised framework for semantic inference from composite audio signals. We focused on the problem of detecting audio scenes and grouping them into meaningful clusters. Our approach addressed all major steps in a general process of composite audio analysis, from low-level signal...