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In recent times, improvements in imaging technology have made available an incredible array of information in image format.
The Materials International Space Station Experiment (MISSE) Project consists of experiments focused on the investigation of the effects that occur through the exposure of materials specimens in the space environment. To determine if there is a change in a specimen, the data obtained from the MISSE high-resolution photographic images can be monitored. For this, a compilation of folders was created to provide an organizational system. Each mission was divided into the specific direction in space, materials specimen, and date the image was taken. This organization helps to identify how a specimen behaves as a function of time in the space environment. Once the images were classified, a qualitative process was first used to detect an evolution in the materials of MISSE-9 - MISSE-14.
Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.
The rocks and soils of the moon will be the raw materials for various engineering needs at a lunar base, such as sources of hydrogen, oxygen, metals, etc. The material of choice for most of the bulk needs is the regolith and its less than 1 cm fraction, the soil. For specific mineral resources it may be necessary to concentrate minerals from either rocks or soils. Therefore, quantitative characterizations of these rocks and soils are necessary in order to better define their mineral resource potential. However, using standard point-counting microscopic procedures, it is difficult to quantitatively determine mineral abundances and virtually impossible to obtain data on mineral distributions within grains. As a start to fulfilling these needs, Taylor et al. and Chambers et al. have developed a procedure for characterization of crushed lunar rocks using x ray digital imaging. The development of a similar digital imaging procedure for lunar soils as obtained from a spectrometer is described.
In order to rapidly and efficiently grow crystals, tools were needed to automatically identify and analyze the growing process of protein crystals. To meet this need, Diversified Scientific, Inc. (DSI), with the support of a Small Business Innovation Research (SBIR) contract from NASA s Marshall Space Flight Center, developed CrystalScore(trademark), the first automated image acquisition, analysis, and archiving system designed specifically for the macromolecular crystal growing community. It offers automated hardware control, image and data archiving, image processing, a searchable database, and surface plotting of experimental data. CrystalScore is currently being used by numerous pharmaceutical companies and academic and nonprofit research centers. DSI, located in Birmingham, Alabama, was awarded the patent Method for acquiring, storing, and analyzing crystal images on March 4, 2003. Another DSI product made possible by Marshall SBIR funding is VaporPro(trademark), a unique, comprehensive system that allows for the automated control of vapor diffusion for crystallization experiments.
In an earlier communication (Munn et al., J Immunol. Methods 166: 11-25, 1993), we presented the initial development of a quantitative assay for monitoring the rates of cellular aggregation based on digital image processing and video microscopy. This study describes some important enhancements and modifications to the procedure. A new index is introduced to characterize the three-dimensional morphology of the aggregates. This index is based on temporal changes in the projected area of the cells and cell aggregates during the course of the experiment. By drawing an analogy with the kinetic theory of gases, we have also introduced a procedure to normalize for variations in cell seeding density among different experiments. In addition, the image analysis technique has been improved by introducing a background subtraction algorithm to remove illumination defects and an adaptive segmentation procedure. These improvements allowed us to completely automate the image analysis procedure, thus minimizing user intervention and improving the reproducibility of the measurements. The enhanced visual assay is evaluated using some recent results from our studies on homotypic lymphocyte aggregation.
Automated Camera Calibration (ACAL) is a computer program that automates the generation of calibration data for camera models used in machine vision systems. Machine vision camera models describe the mapping between points in three-dimensional (3D) space in front of the camera and the corresponding points in two-dimensional (2D) space in the camera s image. Calibrating a camera model requires a set of calibration data containing known 3D-to-2D point correspondences for the given camera system. Generating calibration data typically involves taking images of a calibration target where the 3D locations of the target s fiducial marks are known, and then measuring the 2D locations of the fiducial marks in the images. ACAL automates the analysis of calibration target images and greatly speeds the overall calibration process.
Many embodiments provide a hybrid data processing system (HySDS) of an end-to-end geodetic imaging data system enabling near-real-time science, assessment, response, and rapid recovery. The HySDS may be an operation data processing system that integrates data from many different geodetic data sources and/or sensors, including interferometric synthetic aperture radar (InSAR), GPS, pixel tracking, seismology, and/or modeling, and processes the data to generate actionable high quality science data products. The HySDS may provide for an automated imaging and analysis capabilities that is able to handle the imminent increases in raw data from new and existing geodetic monitoring sensor systems.
Investigations are being conducted with the objective to develop automated numerical image analysis procedures. In this context, an examination is performed of physically-based multispectral data transforms as a means to incorporate a priori knowledge of land radiance properties in the analysis process. A physically-based transform of TM observations was developed. This transform extends the Landsat MSS Tasseled Cap transform reported by Kauth and Thomas (1976) to TM data observations. The present study has the aim to examine the utility of the TM Tasseled Cap transform as applied to TM data from an urban landscape. The analysis conducted is based on 512 x 512 subset of the Washington, DC November 2, 1982 TM scene, centered on Springfield, VA. It appears that the TM tasseled cap transformation provides a good means to explain land physical attributes of the Washington scene. This result provides a suggestion regarding a direction by which a priori knowledge of landscape spectral patterns may be incorporated into numerical image analysis.
An operational system for extracting ice-motion information from synthetic aperture radar (SAR) imagery is being developed as part of the Alaska SAR Facility. This geophysical processing system (GPS) will derive ice-motion information by automated analysis of image sequences acquired by radars on the European ERS-1, Japanese ERS-1, and Canadian RADARSAT remote sensing satellites. The algorithm consists of a novel combination of feature-based and area-based techniques for the tracking of ice floes that undergo translation and rotation between imaging passes. The system performs automatic selection of the image pairs for input to the matching routines using an ice-motion estimator. It is designed to have a daily throughput of ten image pairs. A description is given of the GPS system, including an overview of the ice-motion-tracking algorithm, the system architecture, and the ice-motion products that will be available for distribution to geophysical data users.
We have used digitized scanning electron micrographs and computer image analysis programs to measure track densities in lunar soil grains. Tracks were formed by highly ionizing solar energetic particles and cosmic rays. Back-scattered electron images produced suitable high contrast images for analysis. We used computer counting and measurement of area to obtain track densities. We found an excellent correlation with manual measurements for track densities below 1x10(exp 8) cm(exp -2). For track densities between 1x10(exp 8) to 1x10(exp 9) cm(exp -2) we found that a regression formula using the percentage area covered by tracks gave good agreement with manual measurements. Measurement of tract densities in lunar samples has been a very rewarding technique for measuring exposure ages and soil maturation processes. We have shown that we can reliably measure track densities in lunar grains using image analysis techniques. Automating track counting may allow application of this technique to important problems in regolith dynamics including the ratio of radiation exposure to reworking in various surface and core samples and in regolith breccias.
The Multimission VICAR Planner (MVP) system is an AI planning system which constructs.
A report discusses the continuing development of Windows Interface for Nominal Displacement Selection (WINDS), a computer program for automated analysis of images of the Sun and planets acquired by scientific instruments aboard spacecraft. WINDS is intended to afford capabilities for identification of features, measurement of displacements and velocities, analysis of terrain and of atmospheres, and synthesis of animation sequences of images of terrains and atmospheres from small sets of samples by use of velocity-based interpolation. A major element of WINDS will be a nonlinear correlator capable of tracking small features in complex image sequences. For dynamic image sequences, the correlator will enable compression of data by factors >100. In processing image data, WINDS will take account of such factors as texture in image data, rotation of features during measurement intervals, effects of viewing and solar illumination angles, and vertical structures of atmospheres. WINDS will also take account of positions, aiming directions, and fields of view of cameras to determine three-dimensional feature structures by use of triangulation and stereoscopic analysis techniques.
In recent times, improvements in imaging technology have made available an incredible array of information in image format.
Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.
Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.
Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.
A symbiosis of pyramidal segmentation and morphological transmission is described. The pyramidal segmentation portion of the symbiosis has resulted in low (2.6 percent) misclassification error rate for a one-look simulation. Other simulations indicate lower error rates (1.8 percent for a four-look image). The morphological transformation portion has resulted in meaningful partitions with a minimal loss of fractal boundary information. An unpublished version of Thicken, suitable for watersheds transformations of fractal objects, is also presented. It is demonstrated that the proposed symbiosis works with SAR (synthetic aperture radar) images: in this case, a four-look Seasat image of sea ice. It is concluded that the symbiotic forms of both segmentation and morphological transformation seem well suited for unsupervised geophysical analysis.