Joseph N. Wilson

University of Florida | UF · Department of Computer and Information Science and Engineering

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that i...

Ground penetrating radar (GPR) is used in explosive hazard clearance. Handheld sensors are utilized by operators during search and localization sweeps. These two styles of ground interrogation follow a standard operating procedure (SOP) that attempts to maximize both ground coverage and rate of advance (ROA). As a result, the data collected is non-...

In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale com...

Handheld ground-penetrating radar systems are employed in both military and humanitarian demining operations. Radar system operators are given the difficult job of determining the nature of subsurface objects from signal reflections in real time. Current systems require operators to multitask both collection and classification. This letter tested a...

Ground penetrating radar (GPR) based detection systems have used a variety of different features and machine learning methods to identify buried hazards and distinguish them from clutter and other objects. In this study, we describe a new feature extraction method based on Kolmogorov complexity and information theory. In particular, a three dimensi...

Algorithms developed for the detection of landmines are tasked with discriminating a wide variety of targets in a diverse array of environmental conditions. However, the potential performance of a detection algorithm may be underestimated by evaluating it in batch on a large, diverse dataset. This is because environmental, or in general, contextual...

In this paper, we consolidate and expand upon the current theory and potential applications of the set of $k$ best \emph{cascading via-paths} (CVPs) and the \emph{reciprocal pointer chain} (RPC) method for identifying them. CVPs are a collection of up to $|V|$ paths between a source and a target node in a graph $G = (V,E)$, computed using two short...

Ground penetrating radar (GPR) devices use sensors to capture one-dimensional representations, or A-scans, of the soil and buried properties at each sampling point. Previous work uses reciprocal pointer chains (RPCs) to find onedimensional layers in two-dimensional data (B-scans). We extend this work to find two-dimensional layers in threedimension...

The accurate detection of a diverse set of targets often requires the use of multiple sensor modalities and algorithms. Fusion approaches can be used to combine information from multiple sensors or detectors. But typical fusion approaches are not suitable when detectors do not operate on all of the same locations of interest, or when detectors are...

Macroscopic and microscopic mixture models and algorithms for hyperspectral unmixing are presented. Unmixing algorithms are derived from an objective function. The objective function incorporates the linear mixture model for macroscopic unmixing and a nonlinear mixture model for microscopic unmixing. The nonlinear mixture model is derived from a bi...

Electronic discovery is an interesting sub problem of information retrieval in which one identifies documents that are potentially relevant to issues and facts of a legal case from an electronically stored document collection (a corpus). In this paper, we consider representing documents in a topic space using the well-known topic models such as lat...

In a standard target detection approach, data is collected, points of interest called alarms are identified, and detection algorithms determine the confidence that a target is present at each point. Receiver operating characteristic (ROC) curves can be used to evaluate the performance of each detector and choose operating thresholds. The use of mul...

The Run Packing (RP) fusion method is a novel algorithm that addresses the con dence level fusion problem when M different sensors (or alarm sources) produce alarms independently. The goal of such a fusion method is to map the output confidence range of each alarm source to a global range shared by all of the alarm sources. The shared global confid...

Handheld ground penetrating radar (GPR) devices, such as the AN/PSS-14, produce image data for a detection sequence. Sequences contain sweeps of left to right and right to left swings of the device. By smoothing the image scan and examining local minima, we can determine the sweep ranges and turn around points contained within the data. Different f...

This paper describes an automatic topic extraction, categorization, and relevance ranking model for multi-lingual surveys and questions that exploits machine learning algorithms such as topic modeling and fuzzy clustering. Automatically generated question and survey categories are used to build question banks and category-specific survey templates....

A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear models...

In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss the fundamental models for regression and classification and also their training with the expectation-maximization algorithm. We follow the discussion with improvements to the ME model and focus particularly on the mixtures of Gaussian process experts. We pr...

Identifying the ground surface in ground-penetrating radar (GPR) data is useful and can be done efficiently and accurately using the Viterbi algorithm. This involves representing the radar image as a trellis graph and solving for the optimal path. To identify multiple layer boundaries in a radar image in this manner, it is necessary to find multipl...

Joint Orthogonal Matching Pursuits (JOMP) is used here in the context of landmine detection using data obtained from an electromagnetic induction (EMI) sensor. The response from an object containing metal can be decomposed into a discrete spectrum of relaxation frequencies (DSRF) from which we construct a dictionary. A greedy iterative algorithm is...

A method of incorporating macroscopic and microscopic reflectance models into hyperspectral pixel unmixing is presented and discussed. A vast majority of hyperspectral unmixing methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear models,...

In landmine detection using vehicle-mounted ground-penetrating radar (GPR) systems, ground tracking has proven to be an eective pre-processing step. Identifying the ground can aid in the correction of distortions in downtrack radar data, which can result in the reduction of false alarms due to ground anomalies. However, the air-ground interface is...

When processing ground penetrating radar (GPR) data for the detection of subsurface objects it is common to align the data based on the location of the air-ground interface in order to eliminate the effects of antenna motion. This practice assumes that the ground is mostly flat and that variations in the measured ground locations are primarily due...

Many algorithms have been proposed for detecting anti-tank landmines and discriminating between mines and clutter objects using data generated by a ground penetrating radar (GPR) sensor. Our extensive testing of some of these algorithms has indicated that their performances are strongly dependent upon a variety of factors that are correlated with g...

Multi-kernel learning has become a popular method to allow classification models greater flexibility in representing the relationships between data points. This approach has evolved into localized multi-kernel learning, which creates classification models that have the ability to adapt to a multi-scale feature-space. The advantages of such an appro...

In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GRP signal for alignment purposes. A common simple technique for doing this is to assume that the highest return in an A-scan is from the reflection due to the ground and to use that as the location of the interface. However there are many situ...

In this paper, we propose a new method for performing ground-tracking using ground-penetrating radar (GPR). Ground-tracking involves identifying the air-ground interface, which is usually the dominant feature in a radar image but frequently is obscured or mimicked by other nearby elements. It is an important problem in landmine detection using vehi...

Typical classification models used for detection of buried landmines estimate a singular discriminative output. This classification is based on a model or technique trained with a given set of training data available during system development. Regardless of how well the technique performs when classifying objects that are 'similar' to the training...

It has long been known that there are numerous advantages to sampling images hexagonally rather than rectangularly. However, due to various shortcomings of the addressing schemes, hexagonal sampling for digital images has not been embraced by the mainstream digital imaging community. The idea of using hexagonal sampling for digital imaging applicat...

When users search the deep web, the essence of their search is often found in a previously answered query. The Mor-pheus question answering system reuses prior searches to an-swer similar user queries. Queries are represented in a semi-structured format that contains query terms and referenced classes within a specific ontology. Morpheus answers qu...

Hidden Markov Models (HMMs) have been widely used in landmine detection with Ground Penetrating Radar (GPR) data; however, to the best of our knowledge, there are no other studies that investigated the simultaneous learning of the features and the HMM parameters. In this paper, we present a novel method based on Gibbs sampling that both learns a fe...

This letter presents a simple and fast algorithm to analyze wideband electromagnetic induction data for subsurface targets. A well-known four-parameter model is differentiated, resulting in a two-parameter model. A fast lookup table is used to find parameters as opposed to nonlinear optimization. The proposed approach provides a computationally fas...

The Matching Pursuits Dissimilarity Measure (MPDM) is an effective way to to compare signals that are sparsely approximated using a Matching Pursuits method. The CAMP algorithm uses an MPDM distance measure in Competitive Agglomeration clustering to model and classify signals. The MPDM approach can only compare signals originating from a single sou...

A factor that could affect the performance of ground penetrating radar for landmine detection is self-signature. The radar self-signature is created by the internal coupling of the radar itself and it appears constant in different scans. Although not varying much, the radar self-signature can create hyperbolic shape or anomaly pattern after ground...

It has been shown through various research efforts over the past few decades that there are numerous advantages to sampling images hexagonally rather than rectangularly. Despite the advantages, hexagonal imaging has not been generally accepted as being advantageous due to the lack of sensors that sample hexagonally, lack of displays for hexagonal i...

In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering...

Image algebra is a rigorous, concise notation that unifies linear and nonlinear mathematics in the image domain. Image algebra was developed under DARPA and US Air Force sponsorship at University of Florida for over 15 years beginning in 1984. Image algebra has been implemented in a variety of programming languages designed specifically to support...

The detection of weak scattering plastic landmines using ground penetrating radar (GPR) is a challenging task. This paper presents a few enhancements to the previously proposed subspace spectral correlation feature (SCF) to improve the detection of weak scattering plastic landmines. Preliminary results indicate that the improved subspace SCF techni...

This paper considers the use of data from a wideband electromagnetic induction (EMI) sensor in a prescreener for a landmine detection system employing both ground-penetrating radar (GPR) and EMI sensors. The paper looks at a unique EMI prescreening strategy based on the use of prototypes derived from a training set of landmines. We show that this p...

The discrete Choquet integral is a nonlinear transformation that integrates a real function with respect to a fuzzy measure. We show that the discrete Choquet integral defines a metric if and only if the corresponding measure satisfies certain monotonicity constraints, thereby completely characterizing the class of measures that induce a metric wit...

A variety of algorithms are presented and employed in a hierarchical fashion to discriminate both anti-tank (AT) and anti-personnel (AP) landmines using data collected from wideband electromagnetic induction (WEMI) and ground penetrating radar (GPR) sensors mounted on a robotic platform. The two new algorithms for WEMI are based on the In-phase vs....

Matching pursuits is a well known technique for signal representation and has also been used as a feature extractor for some classification systems. However, applications that use matching pursuits (MP) algorithm in their feature extraction stage are quite problem domain specific, making their adaptation for other types of problems quite hard. In t...

This paper applies the OWA aggregation operator to hand-held GPR data to improve the detection of landmines. Data from a number of sweeps are collected when the hand-held detector is operating in discrimination mode. The energy density spectra of the GPR signal return from individual sweeps are estimated and two OWA aggregation operations are perfo...

The paper discusses the fusing of results from classifiers and discriminant functions. It explores the relationship between the Bayesian opinion pooling of classifier results and the linear pooling of ranks generated by normalizing discriminant values. This work is closely related to current research rank preference aggregation. I discuss a method...

This paper proposes the use of subspace approach to model the energy density spectra (EDS) of landmine targets, for the purpose to improve the detection of weak scattering landmines and their discrimination with clutter objects. The effectiveness of subspace technique to model the landmine EDS depends on the subspace selection. A slight modificatio...

Ground penetrating radar (GPR)-based discrimination of landmines from clutter is known to be challenging due to the wide variability of possible clutter (e.g., rocks, roots, and general soil heterogeneity). This paper discusses the use of GPR frequency-domain spectral features to improve the detection of weak-scattering plastic mines and to reduce...

A variety of algorithms are presented and employed in a hierarchical fashion to discriminate both anti-tank (AT) and anti-personnel (AP) landmines using data collected from wideband electromagnetic induction (WEMI) and ground penetrating radar (GPR) sensors mounted on a robotic platform. The two new algorithms for WEMI are based on the In-phase vs....

A variety of algorithms for the detection of landmines and discrimination between landmines and clutter objects have been presented. We discuss four quite different approaches in using data collected by a vehicle-mounted ground-penetrating radar sensor to detect landmines and distinguish them from clutter objects. One uses edge features in a hidden...

The HSTAMIDS handheld landmine detection system has been used in a number of humanitarian demining activities. Existing algorithms used with this system to assist human in discrimination process do a better job than the operator alone. However, they are unable to model mine and clutter signatures completely, leading to inaccurate mine confidence as...

This paper examines the confidence level fusion of several promising algorithms for the vehicle-mounted ground penetrating radar landmine detection system. The detection algorithms considered here include Edge Histogram Descriptor (EHD), Hidden Markov Model (HMM), Spectral Correlation Feature (SCF) and NUKEv6. We first form a confidence vector by c...

The Borda Count was proposed as a method of ranking candidates by combining the rankings assigned by multiple voters. It has been studied extensively in the context of its original use in political elections and social choice-making. It has recently seen use in machine learning and in ranking web searches, but few of its formal properties have been...

This study looks at application of the Kullback-Leibler distance to classification in landmine discrimination. The paper explores the relationship between the information theoretic concepts of the Kullback-Leibler divergence and mutual information with special attention to the asymmetry of the typical formulation of the Kullback-Leibler distance. I...

The Region Processing Algorithm (RPA) has been developed by the Office of the Army Humanitarian Demining Research and Development (HD R&D) Program as part of improvements for the AN/PSS-14. The effort was a collaboration between the HD R&D Program, L-3 Communication CyTerra Corporation, University of Florida, Duke University and University of Misso...

This paper presents some advances in discrimination and fusion algorithms using metal detector (MD) and ground penetrating radar (GPR) sensors in a robotic wand unit. Previously investigated spatially distributed features are extended and fused with discrete wavelet transform representations of MD data. A multilayer perceptron technique is then ap...

A common approach to training neural network classifiers in a supervised learning setting is to minimize the mean-square error (mse) between the network output for each labeled training sample and some desired output. In the context of landmine detection and discrimination, although the performance of an algorithm is correlated with the mse, it is...

Spectral features generated from GPR measurements have proven to be effective for the discrimination between landmine and clutter objects. Spectral features are extracted from the energy density spectrum estimated from the GPR data at an alarm location. The quality of the energy density spectrum is highly affected by the ground reflection. For deep...

Handheld sensors are commonly used to assist in landmine location and removal. A number of computer systems aimed at assisting humans in discriminating between buried m ines and other objects have been developed. Each such system requires some protocol that involves sweeping th e sensor over a region of ground using some set of patterns to search f...

The AN/PSS-14 (a.k.a. HSTAMIDS) has been tested for its performance in South East Asia (Thailand), South Africa (Namibia) and in November of 2005 in South West Asia (Afghanistan). The system has been proven effective in manual demining particularly in discriminating indigenous, metallic artifacts in the minefields. The Humanitarian Demining Researc...

The paper studies the use of energy density spectra (EDS) derived from ground penetrating radar (GPR) measurements on a sub-surface target to discriminate between landmines and clutter objects. The GPR used to collect the data is frequency swept and has a bandwidth of 1.4 GHz. Our investigation indicates that the EDS reveals distinct characteristic...

We present in this paper the use of frequency domain features deduced from the energy density spectrum to improve the detection of landmines. The energy density spectrum is obtained from the GPR measurements at an alarm location, and a method to estimate the energy density spectrum is proposed. The energy density spectrum is shown to reveal distinc...

Identifying unique patterns of energy in ground penetrating radar images plays an important role in landmine/clutter discrimination. Many different geometric features, including size and the distribution of energy values in a radar image, can be exploited in mine detection and discrimination. The granulometry of a random set (image), computed by me...

We present in this paper the use of frequency domain features deduced from the energy density spectrum to improve the detection of landmines. The energy density spectrum is obtained from the GPR measurements at an alarm location, and a method to estimate the energy density spectrum is proposed. The energy density spectrum is shown to reveal distinc...

Ground penetrating radar is a high-resolution electromagnetic technology that has demonstrated excellent potential for high probability of detection while keeping false alarm rate low for landmine detection in on-road tests. Off-road situations require more advanced methods for dealing with the most significant reflection in GPR data, the ground bo...

An approach to detecting landmines using ground-penetrating radar (GPR) based on feature-based rules, order statistics, and adaptive whitening (FROSAW) is described. FROSAW relies on independent adaptation of whitening statistics in different depths and combining feature-based methods with anomaly detection using rules. Constant false alarm rate (C...

Landmine detection is an important and yet challenging problem remains to be solved. Ground penetrating radar (GPR) is an effective sensor to detect landmines that are made of plastic or have low metal content. Most GPR signal processing algorithms apply processing in the time (depth) domain. This paper proposes to use the frequency domain features...

The ICAT statistics over the past few years have shown at least one out of every five CVE and CVE candidate vulnerabilities have been due to buffer overflows. This constitutes a significant portion of today's computer related security concerns. In this paper we introduce a novel method for detecting stack smashing and buffer overflow attacks. Our r...

An automated methodology for combining Ground Penetrating Radar features from different depths is presented and analyzed. GPR data from the NIITEK system are processed by a depth-dependent, adaptive whitening algorithm. Shape and contrast features, including compactness, solidity, eccentricity, and relative area are computed at the different depths...

An analysis of the utility of region-based processing of Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) is presented. Algorithms for re-sampling GPR data acquired over non-rectangular and non-regular grids are presented. Depth-dependent whitening is used to form GPR images as functions of depth bins. Shape, size, and contrast-ba...

Recently, blind tests of several automated detection algorithms operating on the NIITEK ground penetrating radar data (GPR) have resulted in quite promising performance results. Anecdotally, human observers have also shown notable skill in detecting landmines and rejecting false alarms in this same data; however, the basis of human performance has...

This paper presents some advancement in the detection algorithms using EMI sensor, GPR sensor and their fusion. In the EMI algorithm, we propose the application of the weighted distributed density (WDD) functions on the wavelet domain and the time domain of the EMI data for feature based detection. A multilayer perceptron technique is then applied...

The goal of this paper is to introduce a new area of computer forensics: process forensics. Process forensics involves extracting information from a process's address space for the purpose of finding digital evidence pertaining to a computer crime. The challenge of this sub-field is that the address space of a given process is usually lost long bef...

In this work, we demonstrate the power of providing a common set of operating system services to Grid Architectures, including high-performance I/O, communication, resource management, and process management. A Grid enables the sharing, selection, and aggregation of a wide variety of geographically distributed resources including supercomputers, st...

Abstract - In this work, we demonstrate the power of pro - viding a common set of operating system services to Grid Architectures, including high - performance I/O, communication, resource management, and process management A Grid enables the sharing, selection, and aggregation of a wide variety of geographically distributed resources including sup...

In this work, we demonstrate the power of providing a common set of operating system services to Grid Architectures, including high-performance I/O, communication, resource management and process management. A Grid[1] enables the sharing, selection, and aggregation of a wide variety of geographically distributed resources including supercomputers,...

A variety of sensors have been investigated for the purpose of detecting buried landmines in outdoor environments. Mines with little or no metal are very difficult to detect with traditional mine detection systems. Ground Penetrating Radar (GPR) sensors have shown great promise in detecting low metal mines and can easily detect metal mines. Unfortu...

Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation...

In this document, we discuss the philosophy, theory, and practice of scheduling computational processes on target hardware in the context of the Adaptive Image Manager (AIM) project. Emphasis is placed on (a) operational constraints, (b) high- and low-level structure and functionality of technique, (c) algorithms and statistical analysis, and (d) d...

The present edition differs from the first in several significant aspects. Typographical errors as well as several mathematical errors have been removed. In a number of places the text has been revised to enhance clarity. Several additional algorithms have been included as well as an entire new chapter on geometric image transformations. By popular...

Various researchers have realized the value of implementing loop fusion to evaluate dense (pointwise) array expressions. Recently, the method of template metaprogramming in C++ has been used to significantly speed-up the evaluation of array expressions, allowing C++ programs to achieve performance comparable to or better than FORTRAN for numerical...

The AIM (Adaptive Image Manager) is a client/server based system providing a computer vision and image processing specific protocol that interfaces to potentially numerous varying computing platforms providing image processing and computer vision support. It provides a unified programming interface for the user despite the potential heterogeneity o...

Real-time multi-object detection has been an elusive goal of automated target recognition (ATR) scenarios that employ on- board millimeter-scale processors at low light levels and reduced power requirements. This paper discusses an adaptation of the wedge-and -strip anode to yield design analyses for a silicon wedge-and-strip detector (WESD). In pr...

We report on AVE's research on a new family of smart analog vision chips and ancillary software capable to adaptively select image processing parameters and regions of interest int he field of view. The project aims to adapt the wedge- and-strip (WS) position-sensitive configuration to real- time, multi-object detection. To that end, we use semicon...

SIMD parallel computers have been employed for image related applications since their inception. They have been leading the way in improving processing speed for those applications [1]. However, current parallel programming technologies have not kept pace with the performance growth and cost decline of parallel hardware. A highly usable parallel so...

Many reconfigurable mesh-connected architectures have been proposed for high-performance computing. In this paper, we review the characteristics of two reconfigurable mesh-connected computer models RMESH and PARBS and demonstrate the equivalency of the RMESH with 8-connectivity and the PARBS by giving simulations between them. Keywords: computer ar...

SIMD parallel systems have been employed for image processing and computer vision applications since their inception. This paper describes a system in which parallel programs are implemented using a machine-independent, retargetable object library that provides SIMD execution on the Lockheed Martin PAL-I SIMD parallel processor. Programs' performan...

One of the major obstacles facing developers of parallel image processing applications is the lack of efficient programming environments. In this paper, we describe the environment currently under development for supporting image algebra operations on a fine grained, massively parallel processor, the PAL. A graphical design tool is described as are...

Extracting features of components in an image is an important step for recognition of objects in the image. In this paper, we develop a general formula for extracting some geometric features of image components such as area, perimeter, compactness, height, width, diameter, moments, and centroid. We then design a fast algorithm for the general formu...

this document. The reader is referred to Ritter [1] for a comprehensive treatise covering the mathematics of image algebra

The design of portable image processing algorithms depends on the availability of standard specification languages. In many cases, such specification languages have taken the form of subprogram libraries. In this paper, we discuss a different approach to language standards, namely, the use of a mathematical system, an image algebra, for specifying...

Image algebra has been implemented in a variety of programming languages designed specifically to support the development of image processing and computer vision programs. The University of Florida has been associated with implementations supporting the languages FORTRAN, Ada, and Lisp. Our current work involves the implementation of a class librar...

This chapter discusses the relationship between image algebra and parallel image processing algorithms; it discusses how well single instruction multiple data (SIMD) mesh-connected computers are suited for image algebra. A group of image algebra primitives useful for parallel image processing is selected, and efficient algorithms to implement these...

Convolutions and correlations are fundamental operations in computer vision and image processing. The image-template product expresses convolutions and correlations. In this paper, the authors present an efficient algorithm for the image-template product on SIMD mesh connected computers. The image-template product is computed along disjoint convolu...

The ability to measure gingival volume growth from dental casts would provide a valuable resource for periodontists. This problem is attractive from a computer vision standpoint due to the complexities of data acquisition, segmentation of gingival and tooth surfaces and boundaries, and extraction of features (such as tooth axes) to help solve the c...