Category: Publication

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 been proposed for dimensionality reduction. In this paper we propose a new random projection version […]

Abstract: Hyperspectral target detection algorithms rely on knowing the desired target signature in advance. However, obtaining an effective target signature can be difficult; signatures obtained from laboratory measurements or hand-spectrometers in the field may not transfer to airborne imagery effectively. One approach to dealing with this difficulty is to learn an effective target signature from […]

Abstract: This paper proposes a possibilistic context identification approach for synthetic aperture sonar (SAS) imagery. SAS seabed imagery can display a variety of textures that can be used to identify seabed types such as sea grass, sand ripple and hard-packed sand, etc. Target objects in SAS imagery often have varying characteristics and features due to […]

Abstract: This paper develops an anomaly detection algorithm for subsurface object detection using the handheld ground penetrating radar. The algorithm is based on the Mahalanobis distance measure with adaptive update of the background statistics. It processes the data sequentially for each data sample in a causal manner to generate detection confidences. The algorithm is applied […]

Abstract: In this paper, the Multi-target Extended Function of Multiple Instances (Multi-target eFUMI) method is developed and described. The method is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. Multi-target eFUMI is a generalization of the Function of Multiple Instances approach (FUMI). The FUMI approach differs significantly from standard Multiple […]

Abstract: A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific […]

Abstract: This paper presents an endmember estimation and representation approach for human geography data cubes. Human-related factors that can be mapped for a geographic region include factors relating to population, age, religion, education, medical access and others. Given these hundreds (or even thousands) of factors mapped over a region, it is extremely difficult for an […]

Abstract: Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. […]

Abstract: Side-look synthetic aperture sonar (SAS) can produce very high quality images of the seabed. The imagery generated by SAS sensors display the seafloor and associated textures, such as sand ripples or seagrass. The characterization of SAS imagery is important for applications such as the detection and classification of targets, seabed segmentation and classification, and […]

Abstract: A boundary detection algorithm for synthetic aper- ture sonar (SAS) images that draws upon a popular superpixel formation algorithm is detailed and tested against a set of SAS images containing a variety of common seabed categories. Textural clues are gathered using a novel DP clustering algorithm that replaces the traditional K-means operator used in […]