Abstract: The application of Partial Membership Latent Dirichlet Allocation (PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use… Read More
Conference PapersConference Papers
Partial Membership Latent Dirichlet Allocation for Image Segmentation
Abstract: Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between… Read More
Multiple Instance Dictionary Learning using Functions of Multiple Instances
Abstract: A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that… Read More
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
Abstract: A multiple instance learning (MIL) method, extended Function of Multiple Instances (eFUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The goal of this approach is to learn a personalized heartbeat ”concept” for an individual.… Read More
Multiple Instance Choquet Integral for Classifier Fusion
Abstract: The Multiple Instance Choquet integral (MICI) for classifier fusion and an evolutionary algorithm for parameter estimation is presented. The Choquet integral has a long history of providing an effective framework for non-linear fusion. Previous methods to learn an appropriate… Read More
Alternating Angle Minimization Based Unmixing with Endmember Variability
Abstract: Several techniques exist for dealing with spectral variability in hyperspectral unmixing, such as multiple endmember spectral mixture analysis (MESMA) or compositional models. These algorithms are computationally very involved, and often cannot be executed on problems of reasonable size. In… Read More
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
Abstract: The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space. The space is whitened according to an estimation of the background statistics, which… Read More
Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
Abstract: Many effective supervised discriminative dictionary learning methods have been developed in the literature. However, when training these algorithms, precise ground-truth of the training data is required to provide very accurate point-wise labels. Yet, in many applications, accurate labels are… Read More
On the use of log-gabor features for subsurface object detection using ground penetrating radar
Abstract: Handheld ground penetrating radar (GPR) enables the detection of subsurface objects under different terrains or over regions with significant amount of metal debris. The challenge for the handheld GPR is to reduce the false alarm rate and limit the… Read More
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances
Abstract: The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI… Read More