Ground-penetrating radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. However, sophisticated processing of GPR data is necessary to reduce false alarms due to naturally occurring subsurface clutter and soil distortions. Most currently fielded GPR-based landmine detection algorithms utilize feature extraction and statistical learning to develop robust classifiers capable of discriminating buried threats from inert subsurface structures. Analysis of these techniques indicates strong underlying similarities between efficient landmine detection algorithms and modern techniques for feature extraction in the computer vision literature. This paper explores the relationship between and application of one modern computer vision feature extraction technique, namely histogram of oriented gradients (HOG), to landmine detection in GPR data. The results presented indicate that HOG features provide a robust tool for target identification for both classification and prescreening and suggest that other techniques from computer vision might also be successfully applied to target detection in GPR data. Index Terms-Computer vision, edge histogram descriptors, ground-penetrating radar (GPR), histogram of oriented gradients (HOG), random forest.
Summary Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results. Availability and implementation An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop. Supplementary information Supplementary data are available at Bioinformatics online.
Laser-induced breakdown spectroscopy (LIBS) is an analytical technique real-time geochemical analysis that is being developed for portable use outside of the laboratory. In this study, statistical signal processing and classification techniques were applied to single-shot, broadband LIBS spectra, comprising measured plasma light intensities between 200 and 960 nm, for a suite of 157 garnets of different composition from 92 locations worldwide. Partial least squares discriminant analysis was applied to sets of 25 LIBS spectra for each garnet sample and used to classify the garnet samples based on composition and geographic origin. Careful consideration was given to the cross-validation procedure to ensure that the classification algorithm is robust to unseen data. The results indicate that broadband LIBS analysis can be used to discriminate garnets of different composition and has the potential to discern geographic origin. © 2010
A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG (), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways) (). Additionally, we present the ROBOKOP Knowledge Graph Builder (KGB), which constructs the KG and provides an extensible framework to handle graph query over and integration of federated data sources.
Improved performance in the discrimination of buried threats using Ground Penetrating Radar (GPR) data has recently been achieved using features developed for applications in computer vision. These features, designed to characterize local shape information in images, have been utilized to recognize patches that contain a target signature in two-dimensional slices of GPR data. While these adapted features perform very well in this GPR application, they were not designed to specifically differentiate between target responses and background GPR data. One option for developing a feature specifically designed for target differentiation is to manually design a feature extractor based on the physics of GPR image formation. However, as seen in the historical progression of computer vision features, this is not a trivial task. Instead, this research evaluates the use of convolutional neural networks (CNNs) applied to two-dimensional GPR data. The benefit of using a CNN is that features extracted from the data are a learned parameter of the system. This has allowed CNN implementations to achieve state of the art performance across a variety of data types, including visual images, without the need for expert designed features. However, the implementation of a CNN must be done carefully for each application as network parameters can cause performance to vary widely. This paper presents results from using CNNs for object detection in GPR data and discusses proper parameter settings and other considerations.
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