Differentiation between particulate biological agents and non-biological agents is typically performed via a
time-consuming "wet chemistry" process or through the use of fluorescent and spectroscopic analysis.
However, while these methods can provide definitive recognition of biological agents, many of them have to
be performed in a laboratory environment, or are difficult to implement in the field. Optical recognition
techniques offer an additional recognition approach that can provide rapid analysis of a material in-situ to
identify those materials that may be biological in nature. One possible application is to use these techniques
to "screen" suspicious materials and to identify those that are potentially biological in nature. Suspicious
materials identified by this screening process can then be analyzed in greater detail using the other, more
definitive (but time consuming) analysis techniques. This presentation will describe the results of a feasibility
study to determine whether optical pattern recognition techniques can be used to differentiate biological
related materials from non-biological materials. As part of this study, feature extraction algorithms were
developed utilizing multiple contrast and texture based features to characterize the macroscopic properties of
different materials. In addition, several pattern recognition approaches using these features were tested
including cluster analysis and neural networks. Test materials included biological agent simulants, biological
agent related materials, and non-biological materials (suspicious white powders). Results of a series of
feasibility tests will be presented along with a discussion of the potential field applications for these
Plastic-Bonded Explosives (PBXs) are a newer generation of explosive compositions developed at Los Alamos National Laboratory (LANL). Understanding the micromechanical behavior of these materials is critical. The size of the crystal particles and porosity within the PBX influences their shock sensitivity. Current methods to characterize the prominent structural characteristics include manual examination by scientists and attempts to use commercially available image processing packages. Both methods are time consuming and tedious. LANL personnel, recognizing this as a manually intensive process, have worked with the Kansas City Plant / Kirtland Operations to develop a system which utilizes image processing and pattern recognition techniques to characterize PBX material. System hardware consists of a CCD camera, zoom lens, two-dimensional, motorized stage, and coaxial, cross-polarized light. System integration of this hardware with the custom software is at the core of the machine vision system. Fundamental processing steps involve capturing images from the PBX specimen, and extraction of void, crystal, and binder regions. For crystal extraction, a Quadtree decomposition segmentation technique is employed. Benefits of this system include: (1) reduction of the overall characterization time; (2) a process which is quantifiable and repeatable; (3) utilization of personnel for intelligent review rather than manual processing; and (4) significantly enhanced characterization accuracy.
The aim of this paper is to develop a framework for multi- sensor data fusion for the detection and identification of anti-personnel mines as a part of humanitarian demining project. A two-stage hybrid architecture is proposed to integrate non-homogeneous and dis-similar sensor data from various sensor be in developed as a part of the project. The first stage is used to extract significant information from individual sensor data. Self-organizing neural networks are used to define natural and significant clusters embedded in the sensor data. In this regard two popular self-organizing NN architectures of ART2 and DigNet are studied. The second fusion stage is used to integrate this local sensor information into a global decision. The global decision could be binary as in mine/no-mine decision set, or it could be more complex where identification of the underground mine may be involved. For the present paper, reliable data from different sensor was not available. Extracting different shape feature like moment invariants and Fourier descriptors simulates dis-similar sensor data for simulated shapes. Some results for the performance of the clustering algorithms and the fusion architecture are presented.
Karnal bunt is a fungal disease which infects wheat and, when present in wheat crops, yields it unsatisfactory for human consumption. Due to the fact that Karnal bunt (KB) is difficult to detect in the field, samples are taken to laboratories where technicians use microscopes and methodically search for KB teliospores. AlliedSignal Federal Manufacturing & Technologies, working with the Kansas Department of Agriculture, created a system which utilizes pattern recognition, feature extraction, and neural networks to prototype an automated detection system for identifying KB teliospores. System hardware consists of a biological compound microscope, motorized stage, CCD camera, frame grabber, and a PC. Integration of the system hardware with custom software comprises the machine vision system. Fundamental processing steps involve capturing an image from the slide, while concurrently processing the previous image. Features extracted from the acquired imagery are then processed by a neural network classifier which has been trained to recognize `spore-like' objects. Images with `spore-like' objects are reviewed by trained technicians. Benefits of this system include: (1) reduction of the overall cycle-time; (2) utilization of technicians for intelligent decision making (vs. manual searching); (3) a regulatory standard which is quantifiable and repeatable; (4) guaranteed 100% coverage of the cover slip; and (5) significantly enhanced detection accuracy.
Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance and Detector (MIRADOR) program. MIRADOR is an on/off road, remote control, multisensor system designed to detect buried and surface- emplaced metallic and nonmetallic antitank mines. The basic requirements for this ATR software were the following: (1) an ability to separate target objects from the background in low signal-noise conditions; (2) an ability to handle a relatively high dynamic range in imaging light levels; (3) the ability to compensate for or remove light source effects such as shadows; and (4) the ability to identify target objects as mines. The image segmentation and target evaluation was performed using an integrated and parallel processing approach. Three basic techniques (texture analysis, edge enhancement, and contrast enhancement) were used collectively to extract all potential mine target shapes from the basic image. Target evaluation was then performed using a combination of size, geometrical, and fractal characteristics, which resulted in a calculated probability for each target shape. Overall results with this algorithm were quite good, though there is a tradeoff between detection confidence and the number of false alarms. This technology also has applications in the areas of hazardous waste site remediation, archaeology, and law enforcement.
This paper summarizes the results of a signal taxonomy study of gamma ray burst (GRB) data acquired with sensors on-board the Pioneer-Venus Orbiter (PVO) spacecraft. GRB events produce large fluxes of gamma rays with durations of seconds to minutes and have been observed since the early 1970's. The true nature of GRB's is still unknown, and several competing theories exist. A fundamental point of contention among such theories is whether or not different types of GRB exist. If different types of GRB's are discovered in the existing PVO data base, the differences may correlate with their position or source characteristics. Hence, the goal of this project was to use artificial neural networks to perform signal taxonomy on the GRB data base to determine if unique classes or types of GRB's exist. A total of 26 signal features were identified, some of which can be associated directly with some characteristic of the GRB, such as duration, peak count rate, and gamma ray spectrum hardness. Additional features that were selected included the number of zero crossings in the wavelet transform and the fractal dimension of each signal. A self organizing neural network was used with the signal features to search for correlations among the signals contained in the database. The results of this analysis revealed an intrinsic dimensionality of 2 or 3 in the database. That is, it appears as though 2 or 3 distinct types of GRB may exist. In particular, two of the classes contain roughly 90% of the signals in the database of GRB signals we had to work with. These two classes are similar in characteristics but are still sufficiently distinct from one another to form separate categories. The third class of GRB is definitely distinct from the first two.