The three-dimensional shapes of microscopic objects are becoming increasingly important for battlespace CBRNE
sensing. Potential applications of microscopic 3D shape observations include characterization of biological weapon
particles and manufacturing of micromechanical components. Aerosol signatures of stand-off lidar systems, using
elastic backscatter or polarization, are dictated by the aerosol particle shapes and sizes that must be well characterized in
the lab. A low-cost, fast instrument for 3D surface shape microscopy will be a valuable point sensor for biological
particle sensing applications. Both the cost and imaging durations of traditional techniques such as confocal
microscopes, atomic force microscopes, and electron scanning microscopes are too high.
We investigated the feasibility of a low-cost, fast interferometric technique for imaging the 3D surface shape of
microscopic objects at frame rates limited only by the camera in the system. The system operates at two laser
wavelengths producing two fringe images collected simultaneously by a digital camera, and a specialized algorithm we
developed reconstructs the surface map of the microscopic object. The current implementation assembled to test the
concept and develop the new 3D reconstruction algorithm has 0.25 micron resolution in the x and y directions, and
about 0.1 micron accuracy in the z direction, as tested on a microscopic glass test object manufactured with etching
techniques. We describe the interferometric instrument, present the reconstruction algorithm, and discuss further
We present an algorithm for segmentation of objects with very low edge contrast, such as microcalcifications in mammogram images. Most methods used to segment microcalcifications have algorithmic aspects that could raise operational difficulties, such as thresholds or windows that must be selected manually, or parametric models of the data. The presented algorithm does not use any of these techniques and does not require that any parameters be set by a user. It builds upon an earlier algorithm presented, but is much faster and also applicable to a wider range of objects to be segmented. The algorithm’s approach is based on the extension of radial intensity profiles from a given seed point to the edge of the image. A first derivative analysis is used to find an edge point pixel along each directional intensity profile. These points are connected and the resulting object border is filled using a constrained dilatation operation to form a complete region. Results from the tested mammography images indicate that the segmented regions compare closely to those expected from visual inspection.
The analysis of particles produced by solid rocket motor fuels relates to two types of studies: the effect of these particles on the Earth's ozone layer, and the dynamic flight behavior of solid fuel boosters used by the NASA Space Shuttle. Since laser backscatter depends on the particle size and concentration, a lidar system can be used to analyze the particle distributions inside a solid rocket plume in flight. We present an analytical model that simulates the lidar returns from solid rocket plumes including effects of beam profile, spot size, polarization and sensing geometry. The backscatter and extinction coefficients of alumina particles are computed with the T-matrix method that can address non-spherical particles. The outputs of the model include time-resolved return pulses and range-Doppler signatures. Presented examples illustrate the effects of sensing geometry.
Corner detection is an essential feature extraction step in many image understanding applications including aerial image analysis and manufactured part inspection. Available corner detectors require the user to set critical manual thresholds, degrade under significant noise levels, or introduce high computational complexity. We present a nonlinear corner detection algorithm that does not require prior image information or any threshold to be set by the user. It provides 100% correct corner detection and fewer than 1 false positive corner per image when the contrast to noise ratio of the image is 6 or more, under Gaussian white noise.
A general-purpose remote sensing lidar system model has been developed for use with aerosol targets as well as hard targets in various atmospheric conditions and battlefield aerosol smoke conditions to model the actual analogue return waveform. A description of the model with equations and some of the aerosol parameters are presented. An empirically determined impulse response function from a commercially developed short-range system that operates at 0.905 micrometers is used in the prediction of the analogue output waveform for this particular system. The computed waveforms show the effects of backscatter for aerosol smoke conditions. Some experimental validation of the model for a hard target in military fog oil smoke is shown. This model will be used to predict performance of the current lidar sensor as well as other senors under various other atmospheric and battlefield smoke conditions.
Ballistic missiles can separate in mid-course flight producing several components that include the warhead, control modules, booster segments, and debris. Since many warheads are spin- stabilized, laser radar range-Doppler imaging may provide signatures for identifying the warhead. Discrimination algorithms are most effective when they are based on the signatures expected from the target, however, an analytical model that relates the geometric and physical parameters of the target to its range-Doppler signature has not been available. This study developed a closed-form analytical formulation that models the range-Doppler signatures of a spinning conic warhead as a function of its parameters such as, angular velocity, half-cone angle, height, and aspect angle. Using the 3-D conic surface equation, the angle-of- incidence at an arbitrary point is expressed in terms of the geometric parameters of the target. A relationship that links the Doppler shift to the cross-range coordinate of the target is used to complete the formulation of a point return as a function of range and Doppler. The model predictions match the experimental data well and suggest that this closed-form analytical solution can be used for parameter identification and discrimination in ballistic missile defense.
The widespread and increasing use of mammographic screening for early breast cancer detection is placing a significant strain on clinical radiologists. Large numbers of radiographic films have to be visually interpreted in fine detail to determine the subtle hallmarks of cancer that may be present. We developed an algorithm for detecting microcalcification clusters, the most common and useful signs of early, potentially curable breast cancer. We describe this algorithm, which utilizes contour map representations of digitized mammographic films, and discuss its benefits in overcoming difficulties often encountered in algorithmic approaches to radiographic image processing. We present experimental analyses of mammographic films employing this contour-based algorithm and discuss practical issues relevant to its use in an automated film interpretation instrument.