Abstract: Semi-supervised learning attempts to take advantage of the large amount of unlabeled information present in many datasets. However, unlabeled data will often contain samples outside the classes of interest. Many existing semi-supervised learning methods do not address this issue.… Read More
ThesisThesis
Discriminative Feature Learning with Imprecise, Uncertain, and Ambiguous Data
Abstract: Target detection is a paramount task in remote sensing which aims to detect points of interest from a set of data. A crucial aspect attributed to the success of target detection methods is the representation of the data which… Read More
Weakly Supervised Image Segmentation with Multiple Instance Learning Neural Network
Abstract: In my dissertation, we present multiple instance learning U-net (MILUnet) algorithm and multiple instance learning class activation map (MILCAM) algorithm for weakly supervised semantic segmentation. Both the MILUnet and MILCAM algorithms requires only training images paired with image-level label… Read More
Connecting the Past and the Present : Histogram Layers for Texture Analysis
Abstract: Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG) , local binary patterns (LBP), and edge histogram descriptors (EHD).… Read More
Domain Translation and Image Registration for Multi-Look Synthetic Aperture Sonar Scene Understanding
Abstract: The domain of multi-look scene understanding problems includes scenarios where multiple passes over the same area have occurred and combining information from them is desired. For example, in remotely sensed SAS surveys, the same location on the seafloor is… Read More
Connecting The Past And The Present: Histogram Layers For Texture Analysis
Abstract: Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG) (Dalal and Triggs, 2005), local binary patterns (LBP) (Ojala et… Read More
Classification With Multi-Imprecise Labels
Abstract: Imprecise labels or label uncertainty are common problems in many real supervised and semi-supervised learning problems. However, most of the state-of-the-art supervised learning methods in the literature rely on accurate labels. Accurate labels are often either expensive, time-consuming, or… Read More
SWITCHGRASS GENOTYPE CLASSIFICATION USING HYPERSPECTRAL IMAGERY
Abstract: The adoption of remote sensing techniques in plant science enables noninvasive or minimally invasive measurement, which is also time and labor saving when compared to traditional field measurements. In this thesis, a method to distinguish switchgrass genotypes with the… Read More
ANOMALY AND TARGET DETECTION IN SYNTHETIC APERTURE SONAR
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. Many anomaly and target detection algorithms in the literature have… Read More
LEARNING MULTIPLE TARGET CONCEPTS FROM UNCERTAIN, AMBIGUOUS DATA USING THE ADAPTIVE COSINE ESTIMATOR AND SPECTRAL MATCH FILTER
Abstract: The Multiple Instance Adaptive Cosine Estimator and the Multiple Instance Subspace Match Filter are algorithms used in target detection, where a target class of interest is attempted to be detected amongst a non-target, background class. These algorithms learn a… Read More