Publications

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We present a shape-first approach to finding automobiles and trucks in overhead images and include results from our analysis of an image from the Overhead Imaging Research Dataset [1]. For the OIRDS, our shape-first approach traces candidate vehicle outlines by exploiting knowledge about an overhead image of a vehicle: a vehicle's outline fits into a rectangle, this rectangle is sized to allow vehicles to use local roads, and rectangles from two different vehicles are disjoint. Our shape-first approach can efficiently process high-resolution overhead imaging over wide areas to provide tips and cues for human analysts, or for subsequent automatic processing using machine learning or other analysis based on color, tone, pattern, texture, size, and/or location (shape first). In fact, computationally-intensive complex structural, syntactic, and statistical analysis may be possible when a shape-first work flow sends a list of specific tips and cues down a processing pipeline rather than sending the whole of wide area imaging information. This data flow may fit well when bandwidth is limited between computers delivering ad hoc image exploitation and an imaging sensor. As expected, our early computational experiments find that the shape-first processing stage appears to reliably detect rectangular shapes from vehicles. More intriguing is that our computational experiments with six-inch GSD OIRDS benchmark images show that the shape-first stage can be efficient, and that candidate vehicle locations corresponding to features that do not include vehicles are unlikely to trigger tips and cues. We found that stopping with just the shape-first list of candidate vehicle locations, and then solving a weighted, maximal independent vertex set problem to resolve conflicts among candidate vehicle locations, often correctly traces the vehicles in an OIRDS scene. © 2010 IEEE.

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We demonstrate a new semantic method for automatic analysis of wide-area, high-resolution overhead imagery to tip and cue human intelligence analysts to human activity. In the open demonstration, we find and trace cars and rooftops. Our methodology, extended to analysis of voxels, may be applicable to understanding morphology and to automatic tracing of neurons in large-scale, serial-section TEM datasets. We defined an algorithm and software implementation that efficiently finds all combinations of image blobs that satisfy given shape semantics, where image blobs are formed as a general-purpose, first step that 'oversegments' image pixels into blobs of similar pixels. We will demonstrate the remarkable power (ROC) of this combinatorial-based work flow for automatically tracing any automobiles in a scene by applying semantics that require a subset of image blobs to fill out a rectangular shape, with width and height in given intervals. In most applications we find that the new combinatorial-based work flow produces alternative (overlapping) tracings of possible objects (e.g. cars) in a scene. To force an estimation (tracing) of a consistent collection of objects (cars), a quick-and-simple greedy algorithm is often sufficient. We will demonstrate a more powerful resolution method: we produce a weighted graph from the conflicts in all of our enumerated hypotheses, and then solve a maximal independent vertex set problem on this graph to resolve conflicting hypotheses. This graph computation is almost certain to be necessary to adequately resolve multiple, conflicting neuron topologies into a set that is most consistent with a TEM dataset.

The image created in reflected light DIC can often be interpreted as a true three-dimensional representation of the surface geometry, provided a clear distinction can be realized between raised and lowered regions in the specimen. It may be helpful if our definition of saliency embraces work on the human visual system (HVS) as well as the more abstract work on saliency, as it is certain that understanding by humans will always stand between recording of a useful signal from all manner of sensors and so-called actionable intelligence. A DARPA/DSO program lays down this requirement in a current program (Kruse 2010): The vision for the Neurotechnology for Intelligence Analysts (NIA) Program is to revolutionize the way that analysts handle intelligence imagery, increasing both the throughput of imagery to the analyst and overall accuracy of the assessments. Current computer-based target detection capabilities cannot process vast volumes of imagery with the speed, flexibility, and precision of the human visual system.

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We define a new diagnostic method where computationally-intensive numerical solutions are used as an integral part of making difficult, non-contact, nanometer-scale measurements. The limited scope of this report comprises most of a due diligence investigation into implementing the new diagnostic for measuring dynamic operation of Sandia's RF Ohmic Switch. Our results are all positive, providing insight into how this switch deforms during normal operation. Future work should contribute important measurements on a variety of operating MEMS devices, with insights that are complimentary to those from measurements made using interferometry and laser Doppler methods. More generally, the work opens up a broad front of possibility where exploiting massive high-performance computers enable new measurements.

This report evaluates a newly-available, high-definition, video camera coupled with a zoom optical system for microscopic imaging of micro-electro-mechanical systems. We did this work to support configuration of three document-camera-like stations as part of an installation in a new Microsystems building at Sandia National Laboratories. The video display walls to be installed as part of these three presentation and training stations are of extraordinary resolution and quality. The new availability of a reasonably-priced, cinema-quality, high-definition video camera offers the prospect of filling these displays with full-motion imaging of Sandia's microscopic products at a quality substantially beyond the quality of typical video microscopes. Simple and robust operation of the microscope stations will allow the extraordinary-quality imaging to contribute to Sandia's day-to-day research and training operations. This report illustrates the disappointing image quality from a camera/lens system comprised of a Sony HDC-X310 high-definition video camera coupled to a Navitar Zoom 6000 lens. We determined that this Sony camera is capable of substantially more image quality than the Navitar optic can deliver. We identified an optical doubler lens from Navitar as the component of their optical system that accounts for a substantial part of the image quality problem. While work continues to incrementally improve performance of the Navitar system, we are also evaluating optical systems from other vendors to couple to this Sony camera.