Abstract: Automated anomaly and target detection are commonly used as a prescreening step within a larger target detection and target classification framework to find regions of interest for further analysis. A number of anomaly and target detection algorithms have been… Read More
Month: March 2018
Possibilistic fuzzy local information C-means with automated feature selection for seafloor segmentation
Abstract: The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated… Read More
Congrats to Xiaoxiao for new position as a Senior Research Engineer at the University of Michigan
Congratulations to our lab alumna, Dr. Xiaoxiao Du, on her new appointment as a Senior Research Engineer at the University of Michigan! Dr. Du will be working on machine learning and computer vision methods for pedestrian perception and scene understanding.… Read More
GatorSense Low Rope Course!
The Machine Learning and Sensing Lab had a great time going through the low rope courses together at the Lake Wauburg this past weekend! We had a lot of fun accomplishing every mission together. We followed up our success at… Read More
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
Abstract: In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in… Read More