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At least 55 records · Page 3

Obstacle detection by recognizing binary expansion patterns

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Baram, Yoram↗

DALG: A program for test pattern generation in combinational logical circuits

A user's manual for a computer program DALG which generates test patterns for detecting faults in combinational logic circuits containing up to 200 logical gates is presented. The gates may be of logical types AND, OR, NAND, NOR, NOT, or Exclusive OR is presented. The faults may be any one gate or input struck at a fixed value (0 or 1). In addition to test pattern generation DALG will also determine whether or not the given test pattern will detect given faults in a circuit. Sample problems are given along with input data sheets and printed output to illustrate the capabilities of the program.

Source record↗

A pattern recognition system for locating small volvanoes in Magellan SAR images of Venus

The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

Burl, M. C.↗

Reflected GPS Power for the Detection of Surface Roughness Patterns in Coastal Water

Coastal bays formed by the barrier islands of Delaware, Maryland and Virginia are parts of a coastal region known as a "Coastal Compartment". The coastal compartment between the Chesapeake and Delaware Bays is actually the mosaic of landscapes on the headland of the interfluve that separates these large drainage basins. The coastal compartments form a variety of different-shaped waterways landward of the coastline. Shape differences along the boundaries produce differences in exposure to wind and waves. Different shoreface topographies seaward of the coastline also influence surface roughness by changing wave-refraction patterns. Surface-water roughness (caused by waves) is controlled by a number of parameters, including fetch, shielding, exposure corridors, water-mass boundary conditions, wetland vegetation and water depth in coastal bays. In the coastal ocean, surface roughness patterns are controlled by shoreface shoaling and inlet refraction patterns in the coastal ocean. Knowledge of wave phenomena in the nearshore and backbarrier areas is needed to understand how wave climate influences important ecosystems in estuaries and bays.

Oertel, George, F.↗

Visual detection of spatial contrast patterns: evaluation of five simple models

The ModelFest Phase One dataset is a collection of luminance contrast thresholds for 43 two-dimensional monochromatic spatial patterns confined to an area of approximately two by two degrees. These data were collected by a collaboration among twelve laboratories, and were designed to provide a common database for calibration and testing of spatial vision models. Here I report fits of the ModelFest data with five models: Peak Contrast, Contrast Energy, Generalized Energy, a Gabor Channels model, and a Discrete Cosine Transform model. The Gabor Channels model provides the best fit, though the other, simpler models, with the exception of Peak Contrast, provide remarkably good fits as well. Though there are clear individual differences, regularities in the data suggest the possibility of constructing a standard observer for spatial vision. c2000 Optical Society of America.

NASA Discipline Space Human Factors↗

A preliminary attempt to use neural networks for turbulent eddy classification

This note describes an attempt to use standard neural network tools to fashion a means of detecting eddy patterns in the wall region of a turbulent flow. The research was motivated by the desire to formulate a means to use only flow parameters that can be sensed on the wall to describe the passing eddy structure. If a simple formulation can be obtained, it could conceivably be utilized to control actuators embedded in the wall. Such actuators have been developed by Jacobson and Reynolds (1993a), Blackwelder and Liu (1994), Tung et al. (1995), and others. These actuators have the common characteristics that they are small and are typically flush with the wall when not deployed. When they are activated, it is assumed that they will be able to interact constructively with the turbulent eddies near their location to either decrease the wall shear stress, enhance or reduce the mixing, etc. At present, there is only a nascent understanding of the interaction dynamics between the actuators and the eddies in the flow. Nevertheless, for such interaction to succeed, methods to couple the actuators to the oncoming flow must be obtained. General methods must be found that will detect the space and temporal location of the desired structure. In particular, it will be necessary to know when the eddies will arrive at the location of the actuator. This research attempted to use the shear stress measurements on the wall in the vicinity of an actuator location to predict when a particular eddy pattern would arrive and/or occur at the designated location. In this work the eddy pattern to be detected was identified by its velocity signature only.

Blackwelder, Ron F.↗

Electronic heterodyne recording of interference patterns

An electronic heterodyne technique is being investigated for video (i.e., television rate and format) recording of interference patterns. In the heterodyne technique electro-optic modulation is used to introduce a sinusoidal phase shift between the beams of an interferometer. For phase modulation frequencies between 0.1 and 15 MHz an image dissector camera may be used to scan the resulting temporally modulated interference pattern. Heterodyne detection of the camera output is used to selectively record the interference pattern. An advantage of such synchronous recording is that it permits recording of low-contrast fringes in high ambient light conditions. The application of this technique to the recording of holograms is discussed.

Merat, F. L.↗

Analysis of GOES imagery and digitized data for the SEV-UPS period, August 1979

In support of the Southeastern Virginia Urban Plume Study (SEV-UPS), GOES satellite imagery was analyzed for the month of August 1979. The analyzed GOES images provide an additional source of meteorological input useful in the evaluation of air quality data collected during the month long period of the SEV-UPS experiment. In addition to the imagery analysis, GOES digitized data were analyzed for the period of August 6 to 11, during which a regional haze pattern was detectable in the imagery. The results of the study indicate that the observed haze patterns correspond closely with areas shown in surface based measurements to have reduced visibilities and elevated pollution levels. Moreover, the results of the analysis of digitized data indicate that digital reflectance counts can be directly related to haze intensity both over land and ocean. The model results agree closely with the observed GOES digital reflectance counts, providing further indication that satellite remote sensing can be a useful tool for monitoring regional elevated pollution episodes.

Bowley, C. J.↗

Response of Thematic Mapper bands to plant water stress

Changes in leaf reflectance as water content decreases have been hypothesized to occur in the 1.55-1.75 and 2.08-2.35 micron wavelength regions. To evaluate this hypothesis, studies were conducted on ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L.), which were grown in a controlled, outdoor situation. Both fully-watered control beds and water-stressed beds were periodically examined with a spectroradiometer calibrated against a reflectance reference of polytetrafluoroethylene. The observed changes correspond to those predicted by stochastic leaf models employed by other investigators (leaf reflection increases in the 1.55-1.75 micron region as leaf water content decreases). Although the percentage changes in TM bands 1-3 are nearly as great as those found in TM bands 5 and 7, the absolute values of reflectance change are much lower. It is believed that these patterns are probably characteristic of a broad range of vegetation types. In terms of phenomena detection, these patterns should be considered in any practical remote sensing sensor scenario.

Cibula, W. G.↗

Nondestructive Strain Measurement System Used to Determine Surface Strain on Fibers

Small-diameter structural fibers are being considered as reinforcements for high temperature ceramic matrix composite materials, and thus they require characterization. At the NASA Lewis Research Center, a nondestructive optical technique was used to determine surface strain on a structural fiber, in real time, as it was pulled in a tensile test machine. With this technique, interference or speckle patterns from the laser illuminated fiber test specimen are recorded. As the fiber is pulled, its speckle pattern shifts in proportion to the strain, translation, and rotation components of the sample deformation. Shifting speckle patterns are detected in real time by two linear charge-coupled discharge (CCD) camera arrays, and the images are processed by a hardware correlator. Surface strain is selectively detected on fibers with diameters on the order of 100 mm and can be resolved to 19 microstrain. This system was designed to be robust and compact and generally does not require surface preparation of the structural fibers. For strain detection, two laser beams are positioned incident on the structural fiber being tested, as shown in the photograph, where the test specimen is mounted in a tensile test machine via two coupons. As the fiber is pulled, the speckle pattern produced from each laser beam is detected by one of two CCD arrays located inside the tube on the right side of the photograph.

Tuma, Margaret L.↗

Investigation of light scattering as a technique for detecting discrete soot particles in a luminous flame

The practicability of using a classical light-scattering technique, involving comparison of angular scattering intensity patterns with theoretically determined Mie and Rayleight patterns, to detect discrete soot particles (diameter less than 50 nm) in premixed propane/air and propane/oxygen-helium flames is considered. The experimental apparatus employed in this investigation included a laser light source, a flat-flame burner, specially coated optics, a cooled photomultiplier detector, and a lock-in voltmeter readout. Although large, agglomerated soot particles were detected and sized, it was not possible to detect small, discrete particles. The limiting factor appears to be background scattering by the system's optics.

Source record↗

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Tescher, Andrew G.↗

Damage Detection Using Holography and Interferometry

This paper reviews classical approaches to damage detection using laser holography and interferometry. The paper then details the modern uses of electronic holography and neural-net-processed characteristic patterns to detect structural damage. The design of the neural networks and the preparation of the training sets are discussed. The use of a technique to optimize the training sets, called folding, is explained. Then a training procedure is detailed that uses the holography-measured vibration modes of the undamaged structures to impart damage-detection sensitivity to the neural networks. The inspections of an optical strain gauge mounting plate and an International Space Station cold plate are presented as examples.

Decker, Arthur J.↗

The use of ERTS-1 satellite data in Great Lakes mesometeorological studies

The author has identified the following significant results. In the original proposal, it was hoped that ERTS could, with its extremely high resolution and multispectral capability, detect many meteorological phenomena occurring at the low end of the mesoscale motion spectrum (1 - 100 km). This included convective cloud phenomena, internal wave patterns, air pollution, snow squalls, etc. For meteorologists, ERTS-1 has more than lived up to initial hopes. First-look inspection of images has produced a large number of truly remarkable finds. Some of the most significant are: (1) Images of Lake Ontario during late summer have revealed several extremely good examples of lake breeze frontal cloud patterns. (2) Detection of suspended particulates from Chicago-Gary industrial complex in the 50,000 to 150,000 tons/year category. (3) Inadvertant weather modification due to anthropogenic condensation and ice nuclei from urban areas.

Lyons, W. A.↗

Coherent summation of spatially distorted laser Doppler signals by using a two-dimensional heterodyne detector array

Phase-sensitive coherent summation of individual heterodyne detector array signals was demonstrated for the enhanced detection of spatially distorted laser Doppler returns. With the use of a 2 x 2 heterodyne detector array, the phase and amplitude of a time-varying speckle pattern was detected, and the signal-to-noise ratio of the Doppler shift estimate was shown to be improved by a factor of 2, depending on the extent of spatial coherence loss. These results are shown to agree with a first-order analysis and indicate the advantage of coherent summation for both short-range laser Doppler velocimetry and long-range atmospheric coherent lidar.

Chan, Kin P.↗

Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

Landsat satellite imagery was analyzed to generate a detailed record of 10 years of vegetation disturbance and regrowth for Pacific coastal areas of Marin and San Francisco Counties. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology, a transformation of Tasseled-Cap data space, was applied to detected changes in perennial coastal shrubland, woodland, and forest cover from 1999 to 2009. Results showed several principal points of interest, within which extensive contiguous areas of similar LEDAPS vegetation change (either disturbed or restored) were detected. Regrowth areas were delineated as burned forest areas in the Point Reyes National Seashore (PRNS) from the 1995 Vision Fire. LEDAPS-detected disturbance patterns on Inverness Ridge, PRNS in areas observed with dieback of tanoak and bay laurel trees was consistent with defoliation by sudden oak death (Phytophthora ramorum). LEDAPS regrowth pixels were detected over much of the predominantly grassland/herbaceous cover of the Olema Valley ranchland near PRNS. Extensive restoration of perennial vegetation cover on Crissy Field, Baker Beach and Lobos Creek dunes in San Francisco was identified. Based on these examples, the LEDAPS methodology will be capable of fulfilling much of the need for continual, low-cost monitoring of emerging changes to coastal ecosystems.

Land Cover Change↗

Flightspeed Integral Image Analysis Toolkit

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Thompson, David R.↗

Ecosystem Impacts of Woody Encroachment In Texas: A Spatial Analysis Using AVIRIS

Woody encroachment, the increase of woody plant density relative to herbaceous vegetation, has been documented in drylands of Texas as well as worldwide (Archer 1994, Harrington and Harman 1995, Moleele et al. 2002). Over-grazing, fire suppression and climate change are implicated in the shift from open grasslands to ecosystems now populated by trees and shrubs (Scholes and Archer 1997, Archer et al. 2001), such as Prosopis glandulosa var. glandulosa (honey mesquite) in north Texas (Teague et al. 1997, Ansley et al. 2001, Asner et al. 2003a). Several studies have examined changes in ecosystem properties accompanying woody vegetation encroachment in the Southwest U.S., with research focused on increases in plant and soil carbon (C) and nitrogen (N) stores (Hoffman and Jackson 2000, Asner et al. 2003a), isotopic shifts in these pools (Boutton 1999, Archer et al. 2001), and increases in N cycling rates (Rundel et al. 1982, Hibbard et al. 2001). However, little is known regarding the impact of woody encroachment on N trace gas emissions from dryland regions such as Texas. NOx is produced in the soil during the processes of nitrification and denitrification (Firestone and Davidson 1989). The total N efflux from soils is most directly influenced by the internal cycling of N, which at a regionalscale, is controlled by the inputs and availability of N from vegetation via litterfall and subsequent decomposition (Robertson et al. 1989). Although plot-scale studies are critical to understanding controls over N oxide emissions, regionalization of the measurements is impeded by spatial variation in the factors contributing most to N cycling processes: soil properties (affecting soil moisture regimes and N stocks) and vegetation cover (affecting litter inputs and N uptake). While broad patterns in ecosystem structure and vegetation composition co-vary with general patterns of trace gas emissions (Matson 1997), there is no easily measured index of N availability that can be applied for regional-scale studies of N oxide fluxes. Remote sensing is arguably the only approach available to develop a spatially-explicit understanding of ecosystem processes. More specifically, remotely detectable spatial patterns in the distal controls over soil N properties, such as vegetation cover, land use and soil type (Robertson et al. 1989), should be exploited for regional studies of N oxide emissions. The woody encroachment phenomenon provides an opportunity to test the strength of the relationship between N oxide emissions and those factors controlling the fluxes that can be remotely measured. If such linkages can be firmly established, and if the spatial pattern of distal controls is relevant, then the combination of field measurements and remote sensing offers to improve regional-scale N oxide estimates. The paper presents the utility of linking field based sampling of soil NOx emissions with very high resolution remote sensing estimates of woody vegetation cover from the NASA AVIRIS, Airborne Visible-Infrared Imaging Spectrometer (Green et al. 1998, Asner and Green 2001) and automated spectral mixture analysis (Asner and Lobell 2000, Asner and Heidebrecht 2002) that provide a means to spatially extrapolate soil NOx emissions to the regional scale.

Martin, Roberta E.↗