Ms. Kim Dalton Linder Profile

Kim Dalton Linder

Engineer at Honeywell Inc

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Publications (3)

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Proceedings Article | 16 April 2008 Paper

Optical recognition of biological agents

Chris Baumgart, Kim Dalton Linder, Josh Trujillo

Proceedings Volume 6969, 696909 (2008) https://doi.org/10.1117/12.784628

KEYWORDS: Biological weapons, Biological research, Biomedical optics, Feature extraction, Detection and tracking algorithms, Neural networks, Imaging systems, Pattern recognition, Algorithm development, Image acquisition

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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 techniques.

Proceedings Article | 24 August 2006 Paper

Autonomous characterization of plastic-bonded explosives

Kim Dalton Linder, Paul DeRego, Antonio Gomez, Chris Baumgart

Proceedings Volume 6312, 631215 (2006) https://doi.org/10.1117/12.680490

KEYWORDS: Crystals, Image segmentation, Image processing, Explosives, Image analysis, CCD cameras, Epoxies, Zoom lenses, Data acquisition, Imaging systems

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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.

Proceedings Article | 2 February 1998 Paper

Automated detection of Karnal bunt teliospores

Kim Linder, Chris Baumgart, Jim Creager, Bob Heinen, Tim Troupe, Dick Meyer, Janie Carr, Jack Quint

Proceedings Volume 3306, (1998) https://doi.org/10.1117/12.301233

KEYWORDS: Image processing, Neural networks, Microscopes, Feature extraction, Agriculture, Imaging systems, CCD cameras, Video, Manufacturing, Image segmentation

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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.

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