Tag: Choquet Fuzzy Integral

Congratulations to Gatorsense alumna, Xiaoxiao Du!  Her paper, titled “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels”, was recently accepted for publication with The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019).  Xiaoxiao will present her work at the conference in Xiamen, China later this […]

Abstract: Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require […]

Abstract: In underwater synthetic aperture sonar (SAS) imagery, there is a need for accurate target recognition algorithms. Automated detection of underwater objects has many applications, not the least of which being the safe extraction of dangerous explosives. In this paper, we discuss experiments on a deep learning approach to binary classification of target and non-target […]

Abstract: The ability to discern the characteristics of the seafloor has many applications. Due to minimal visibility, Synthetic Aperture Sonar Imagery (SAS) uses sonar to produce a texture map of the seabed below. In this paper, we discuss an approach to detecting targets from varying seafloor contexts. The approach begins with one or more anomaly […]