What deep learning lacks at the moment is the heterogeneous and dynamic capabilities of
the human system. In part, this is because a single architecture is not currently capable of …

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine
learning. However, a serious limitation of kernel methods is knowing which kernel is needed …

Countless challenges in engineering require the intelligent combining (aka fusion) of data or
information from multiple sources. The Choquet integral (ChI), a parametric aggregation …

It is widely accepted that feature extraction is quite possibly the most critical step in computer
vision. Typically, feature extraction is performed using a method such as the histogram of …

Abstract The Choquet integral (ChI), a parametric function for information aggregation, is
parameterized by the fuzzy measure (FM), which has 2^ N 2 N real-valued variables for N …

Object recognition is a critical component in most computer vision applications, specifically
image classification tasks. Often, it is desired to design an approach that either learns from …

Radiant energy object detection deep learning algorithms require large training sets with
site-specific images, often from locations that are difficult to access, while also remaining …

In this dissertation, image feature extraction quality is enhanced through the introduction of
two feature learning techniques and, subsequently, feature-level fusion strategies are …

Vehicle maneuverability is often supported in low-light scenarios through infrared (IR)
imagery. However, if the imagery contains little temperature gradient, the raw images are …

Data/information fusion is an integral component of many existing and emerging
applications; eg, remote sensing, smart cars, Internet of Things (IoT), and Big Data, to name …