Abstract: For many years, topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting and recognizing objects in imagery simultaneously. However, these models are confined to the analysis of categorical data, forcing a visual word to belong to… Read More
PublicationPublication
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
Task Driven Extended Functions of Multiple Instances
Abstract: Dictionary learning techniques have proven to be a powerful method in the pattern recognition literature. Recently supervised dictionary learning has been used to achieve very good results on a number of different data types and applications. However, these supervised… Read More
Sand ripple characterization using an extended synthetic aperture sonar model and parallel sampling method
Abstract: The aim of this work is to characterize the seafloor by estimating invariant sand ripple parameters from synthetic aperture sonar (SAS) imagery. Using a hierarchical Bayesian framework and a known sensing geometry, a method for estimating sand ripple frequency,… Read More
Bayesian fuzzy clustering
Abstract: We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present… Read More
Functions of Multiple Instances for Learning Target Signatures
Abstract: The functions of multiple instances (FUMI) approach for learning target and nontarget signatures is introduced. FUMI is a generalization of the multiple-instance learning (MIL) approach for supervised learning. FUMI differs significantly from standard MIL and supervised learning approaches because… Read More
Random projections fuzzy c-means (RPFCM) for big data clustering
Abstract: Many contemporary biomedical applications such as physiological monitoring, imaging, and sequencing produce large amounts of data that require new data processing and visualization algorithms. Algorithms such as principal component analysis (PCA), singular value decomposition and random projections (RP) have… Read More