A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint
Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical
contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and
bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed
from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one
measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are
expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be
applied. The limitations of applying this framework to chemical detection problems are discussed along with means to
mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi
techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several
figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared.
Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC
techniques are employed to optimize chemical-specific parameters to further improve performance.
Various chemical agents have been known to provide unique Raman spectrum signatures. Practical methods for
chemical detection have to deal with cluttered data where the desired agent's signature is mixed with those of
other chemicals in the immediate environment. It has been found that unmixing is affected by strong background
signatures, such as those from the substrate, and noise. This work investigates use of wavelet transform based
techniques for denoising and baseline correction for the purpose of enhancing the probability of detection of a
A Chem/Bio Defense Algorithm Benchmark is proposed as a way to leverage algorithm expertise and apply it to high
fidelity Chem/Bio challenge problems in a high fidelity simulation environment. Initially intended to provide risk
mitigation to the DTRA-sponsored US Army CUGR ACTD, its intent is to enable the assessment and transition of
algorithms to support P3I of future spiral updates. The key chemical sensor in the CUGR ACTD is the Joint
Contaminated Surface Detector (JCSD), a short-range stand-off Raman spectroscopy sensor for tactical in-the-field
applications. The significant challenges in discriminating chemical signatures in such a system include, but are not
limited to, complex background clutter and low signal to noise ratios (SNR). This paper will present an overview of the
Chem-Bio Defense Algorithm Benchmark, and the JCSD Challenge Problem specifically.
ITT Corporation, Advanced Engineering and Sciences Division, is currently developing the Joint Contaminated Surface
Detector (JCSD) technology under an Advanced Concept Technology Demonstration (ACTD) managed jointly by the
U.S. Army Research, Development, and Engineering Command (RDECOM) and the Joint Project Manager for Nuclear,
Biological, and Chemical Contamination Avoidance for incorporation on the Army's future reconnaissance vehicles.
This paper describes the design of the chemical agent identification (ID) algorithm associated with JCSD. The algorithm
detects target chemicals mixed with surface and interferent signatures. Simulated data sets were generated from real
instrument measurements to support a matrix of parameters based on a Design Of Experiments approach (DOE).
Decisions based on receiver operating characteristics (ROC) curves and area-under-the-curve (AUC) measures were
used to down-select between several ID algorithms. Results from top performing algorithms were then combined via a
fusion approach to converge towards optimum rates of detections and false alarms. This paper describes the process
associated with the algorithm design and provides an illustrating example.
Phase correlation is applied to the mosaicing of confocal scanning laser microscopy data. A large specimen (i.e., a murine heart) is cut into a number of individual sections with appropriate thickness. The sections are scanned horizontally and vertically to produce tiles of a 3D volume. Image processing based on phase correlation is used to rebuild the 3D volume and stitch the tiles together. Specifically, 2D registration of in-plane tiles and 3D alignment of optical slices within a given physical section are performed. The approach and performance are presented in this paper along with examples.
The goal of this work is to provide bomb squad units with innovative algorithms for the automatic detection and highlight of blasting caps and other components within x-ray imagery. In this paper, an integrated image processing algorithm, referred to as X-ray Interpreter (XI) is presented that autonomously analyzes data collected by an x-ray imager. The algorithm detects and recognizes a blasting cap of reference in the image, and highlights its location whenever present. This process will assist the operator to focus more on regions within the regions within the image that are highlighted by the algorithm. Examples of applying the algorithm to real data are shown along with performance measure curves for detection and false alarm probabilities.
Different types of shape parameters based on circularity, Fourier descriptors, and invariant moments are studied for the automatic detection of weapons in Millimeter-wave data. First, performance of the shape descriptors is evaluated on simulated objects. The best performing shape descriptors are then tested on the automatic recognition of weapons in real data.
Several image processing procedures have been used for the enhancement and detection of weapons concealed underneath clothing in millimeterwave data. Specifically, registration, fusion, tracking, enhancement, segmentation, and recognition procedures have been successfully tested. These procedures are reviewed in this paper along with examples of their application.
Shape parameters based on circularity, Fourier descriptors, and invariant moments are studied for the automatic detection of weapons in millimeter-wave data. The data is collected by a 30-frames-per-second millimeter-wave (MMW) imager manufactured by Trex Enterprises for the detection of weapons concealed underneath a person's clothing. Results are illustrated through processing real MMW data.
A MMW sensor developed by Trex Enterprises generates image data of a person hiding a gun under his clothing at a distance of 27 feet. The goal of this research was to develop an algorithm that would automatically recognize the weapon. Tracking, segmentation, and recognition procedures were designed and successfully applied to the data.
A number of sensors are being developed for the Concealed Weapon Detection, and use of the appropriate sensor or combination of sensor will be very important to the success of such technologies. Assuming that two identical sensors are used to collect data on a target from different angular views, this paper addresses the problem of registration associated with the collected scenes. Theory and application to real data are presented.
A number of technologies are being developed for concealed weapon detection (CWD), and use of the appropriate processing techniques will be very important to the success of such technologies. In this paper, signal processing procedures used t enhance the detection of weapons concealed underneath clothing are described and illustrated.
This paper addresses the problem of finding the important thresholds in a scene for the detection of concealed weapon(s). Whenever the weapon's temperature is very close to that of the human body, the intensities in the area of the weapon are close to those of the human body. Thus, in a histogram, the intensities of the weapon area overlap with those of the human body causing (1) the weapon's intensities not to be identifiable in the histogram of the overall scene, and (2) the weapon not to be visually distinguishable in the scene. This problem is addressed by the mapping procedure of A'SCAPE. The procedure automatically detects all important thresholds in the scene including those separating regions with overlapping histograms. Real data of an IR scene is used to illustrate the procedure.
A wide variety of concealed weapon detection systems are being investigated to determine the potential payoffs of employing these sensors to detect weapons concealed under a person's clothing. The enabling sensing mechanisms being studied include infrared, acoustic, millimeter wave, and X- ray sensors. The primary emphasis of this paper is on infrared. A new technique for processing sensor data by partitioning non-homogeneous images into homogeneous regions for later detection and identification processing is presented. The name of this method is Automated Statistical Characterization and Partitioning of Environments (A'SCAPE). A'SCAPE enables image enhancement for reliable detection and identification of weapons concealed under varying layers of clothing through its mapping process. By employing a variety of sensors, another enabling technology for concealed weapon detection (CWD) is sensor fusion. Concepts for experiments and analysis are discussed to determine the feasibility of sensor fusion approaches for CWD.
Improvement in the capabilities of infrared, millimeter- wave, acoustic, and x-ray, sensors has provided means to detect weapons concealed beneath clothing and to provide wide-area surveillance capability in darkness and poor light for military special operations and law enforcement application. In this paper we provide an update on this technology, which we have discussed in previous papers on this subject. We present new data showing simultaneously obtained infrared and millimeter-wave images which are especially relevant because a fusion of these two sensors has been proposed as the best solution to the problem of concealed weapon detection. We conclude that the use of these various sensors has the potential for solving this problem and that progress is being made toward this goal.
The statistical characterization of complex real-world backgrounds is a crucial issue in the design of effective detection algorithms. The approach taken here is to monitor the environment and divide it into homogeneous partitions which are characterized by their probability distributions. A new technique for characterizing multivariate random data is described and the effectiveness of the approach is illustrated by two applications: concealed weapon detection and weak signal detection in strong non-Gaussian clutter.
This paper discusses some new enabling technologies for law enforcement and security. A wide variety of concealed weapon detection systems are being investigated to determine the potential payoffs of employing these sensors to detect weapons concealed under a person's clothing. The enabling sensing mechanisms being studied include infrared, millimeter wave, acoustic, and x-ray sensors. The primary emphasis of this paper is on infrared and millimeter wave. A new technique for processing sensor data by segmenting and partitioning non-homogeneous images into homogeneous regions for later detection and identification processing is described. The name of this method is automated statistical characterization and partitioning of environments (A'SCAPE). A'SCAPE enables image enhancement for reliable detection and identification of weapons concealed under varying layers of clothing through its mapping process. By employing a variety of sensors, another enabling technology for concealed weapon detection (CWD) is sensor fusion. Concepts for experiments and analysis are discussed to determine the feasibility of various sensor fusion approaches for CWD.
Amid the areas of application that the military has identified to transfer its technologies to is that of the law enforcement where they are interested in the application of different types of imaging sensors, particularly the millimeter wave sensors, to the detection of weapons concealed underneath clothing. In order to detect concealed weapons underneath clothing, it is important for the sensor to (1) have a good heavy clothing penetration, (2) operate at a reasonable long range and (3) display data in a near real time. Because these three prerequisites cannot be accomplished simultaneously, we introduce in this work a signal processing stage referred to as the Automated Statistical Characterization and Partitioning of Environments procedure (A'SCAPE), to be located between the data collection and the data display stages of the sensor in order to enhance the collected data before it is displayed. In this paper advantages and inconveniences of passive millimeter-wave sensors are discussed, A'SCAPE is described and an illustrating example is presented.
Using thresholding techniques it is possible to separate between contiguous non-homogeneous patches with different power levels. When the power levels of the patches are similar if not equal, the global histogram of the patches is unimodal and the thresholding approach becomes very difficult if not impossible. In this paper, we propose to use a statistical procedure to separate between contiguous non-homogeneous patches with similar power levels but different data statistics. The procedure separates different regions by distinguishing between their data probability distributions. The procedure is based on the Ozturk algorithm which uses the sample order statistics for the approximation of univariate distributions.
In signal processing applications it is common to assume a Gaussian problem in the design of optimal signal processors. However, non-Gaussian processes do arise in many situations. When the possibility of a non-Gaussian problem is encountered, the question as to which probability distributions should be utilized in a specific situation for modeling the data needs to be answered. In practice, the underlying probability distributions are not known a priori. Consequently, an assessment must be made by monitoring the environment to subdivide for each patch. In this paper, an automatic statistical characterization and partitioning of environments process, previously used on simulated data, is applied to real data of an IR image. Two separate procedures are used to determine all homogeneous patches and subpatches in the IR image. The first procedure, referred to as the mapping procedure, is used to separate contiguous homogeneous regions by segregating between their power levels. The second procedure, referred to as the statistical procedure, separates contiguous homogeneous regions by segregating between their probabilistic data distributions. The latter procedure makes use of Ozturk algorithm, a newly developed algorithm for analyzing random data. Furthermore, the statistical procedure identifies suitable approximations to the probability density function for each homogeneous patch and determines the location of outliers. Convergence of the procedures is controlled by an expert system shell.