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At least 325 records · Page 18

Automated Recognition Of Melt Lines In Phase-Change Paints

Technique developed to reduce data quickly and objectively in images of phase-change paints. Involves use of three-tube color-camera system. Camera modified so separate red (R), green (G), and blue (B) video outputs contain complete framing information recorded and digitized by use of standard video format. Proven very simple and repeatable. Used wherever rapid and accurate remote temperature-profile measurements desired; for example, for test objects in inaccessible, toxic, or other hostile areas.

Buck, Gregory M.↗

Multispectral analysis of limestone, dolomite, and granite, Mill Creek, Oklahoma

Spectral reflectance and thermal emission data were collected at the Mill Creek, Oklahoma test site during NASA missions 132 and 133 in June 1970. The data were collected by three aircraft flown several times during the diurnal cycle at altitudes of 150 to 17,000 m above mean terrain. Reflectance of the main rock types (limestone, dolomite, and granite) was determined from the data collected using a 12-channel multispectral scanner during mission 133 and from thermal infrared images recorded during mission 132 on an RS-7 scanner from 17,000 m above terrain. A preliminary rock recognition map was generated automatically using data collected from 900 m above terrain. The discrimination provided by the map is reasonably accurate. Misidentification occurred in areas of unusually high dolomite reflectivity. High altitude thermal infrared (10 to 12 micrometers) images show regional folds and faults distinguished by the presence of thermally contrasting materials. Linear and curvilinear structural features two to three times smaller than the nominal 17 m resolution could be detected.

Rowan, L. C.↗

Software and Algorithms for Biomedical Image Data Processing and Visualization

A new software equipped with novel image processing algorithms and graphical-user-interface (GUI) tools has been designed for automated analysis and processing of large amounts of biomedical image data. The software, called PlaqTrak, has been specifically used for analysis of plaque on teeth of patients. New algorithms have been developed and implemented to segment teeth of interest from surrounding gum, and a real-time image-based morphing procedure is used to automatically overlay a grid onto each segmented tooth. Pattern recognition methods are used to classify plaque from surrounding gum and enamel, while ignoring glare effects due to the reflection of camera light and ambient light from enamel regions. The PlaqTrak system integrates these components into a single software suite with an easy-to-use GUI (see Figure 1) that allows users to do an end-to-end run of a patient s record, including tooth segmentation of all teeth, grid morphing of each segmented tooth, and plaque classification of each tooth image. The automated and accurate processing of the captured images to segment each tooth [see Figure 2(a)] and then detect plaque on a tooth-by-tooth basis is a critical component of the PlaqTrak system to do clinical trials and analysis with minimal human intervention. These features offer distinct advantages over other competing systems that analyze groups of teeth or synthetic teeth. PlaqTrak divides each segmented tooth into eight regions using an advanced graphics morphing procedure [see results on a chipped tooth in Figure 2(b)], and a pattern recognition classifier is then used to locate plaque [red regions in Figure 2(d)] and enamel regions. The morphing allows analysis within regions of teeth, thereby facilitating detailed statistical analysis such as the amount of plaque present on the biting surfaces on teeth. This software system is applicable to a host of biomedical applications, such as cell analysis and life detection, or robotic applications, such as product inspection or assembly of parts in space and industry.

Talukder, Ashit↗

Detection of Water Hazards for Autonomous Robotic Vehicles

Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.

Matthes, Larry↗

A boundary finding algorithm and its applications

An algorithm for locating gray level and/or texture edges in digitized pictures is presented. The algorithm is based on the concept of hypothesis testing. The digitized picture is first subdivided into subsets of picture elements, e.g., 2 x 2 arrays. The algorithm then compares the first- and second-order statistics of adjacent subsets; adjacent subsets having similar first- and/or second-order statistics are merged into blobs. By continuing this process, the entire picture is segmented into blobs such that the picture elements within each blob have similar characteristics. The boundaries between the blobs comprise the boundaries. The algorithm always generates closed boundaries. The algorithm was developed for multispectral imagery of the earth's surface. Application of this algorithm to various image processing techniques such as efficient coding, information extraction (terrain classification), and pattern recognition (feature selection) are included.

Gupta, J. N.↗

Interlobate comparison of glacial-depositional style as evidenced by small-relief glacial landscape features in Illinois, Indiana, and Ohio, utilizing SIR-B

The objectives and expected results of an investigation of the use of the Shuttle Imaging Radar-B (SIR-B) as a basic tool in the recognition and mapping of glacial landforms are discussed. The main goals are: (1) to evaluate the ability of SIR-B to delineate varying sizes, shapes, and relief of surface forms; (2) to compare and contrast SIR-B imagery with selected Seasat SAR imagery; (3) to utilize SIR-B imagery synergistically with available SEASAT SAR, LANDSAT RBV, and other imagery sources to identify and map suites of glacial landforms; and (4) eventually to interpret the suites in terms of ice dynamics and conditions of deglaciation, to relate them to the stratigraphic record, and to evaluate interactions of the major lobes and sublobes.

Johnson, W. H.↗

Airborne reconnaissance XIII; Proceedings of the Meeting, San Diego, CA, Aug. 7-9, 1989

The present conference on airborne reconnaissance discusses topics in imagery exploitation, reconsystem modeling and analysis, and reconnaissance optics and electronics configurations. Attention is given to airborne minefield detection, the optimization of an IR linescanner for RPV operations, real-time display of IR linescanner data for RPVs, three-dimensional model-guided site recognition, the AMIDARS high-performance real-time display, and MMW sensor image analysis. Also discussed are reconnaissance concepts for the 3-5 micron spectral window, sensor concept development for hazard detection, a stabilization system for a large aperture camera, three-axis image stabilization with a two-axis mirror, the results of performance tests on the TOW target collimator design, and the replacement of film by electrooptic media in advanced tactical airborne reconnaissance.

Henkel, Paul A.↗

Feature isolation and quantification of evolving datasets

Identifying and isolating features is an important part of visualization and a crucial step for the analysis and understanding of large time-dependent data sets (either from observation or simulation). In this proposal, we address these concerns, namely the investigation and implementation of basic 2D and 3D feature based methods to enhance current visualization techniques and provide the building blocks for automatic feature recognition, tracking, and correlation. These methods incorporate ideas from scientific visualization, computer vision, image processing, and mathematical morphology. Our focus is in the area of fluid dynamics, and we show the applicability of these methods to the quantification and tracking of three-dimensional vortex and turbulence bursts.

Source record↗

Bio-Inspired Neural Model for Learning Dynamic Models

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Duong, Tuan↗

State-Estimation Algorithm Based on Computer Vision

An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.

Bayard, David↗

Pattern-Recognition Processor Using Holographic Photopolymer

proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square-law detector for the purpose of holographic recording of interference fringes. A typical state-of-the-art CCD has a pixel-pitch limited resolution of about 100 lines/mm. In contrast, the holographic photopolymer to be used in the proposed JTOC offers a resolution > 2,000 lines/mm. In addition to being disadvantageous in itself, the low resolution of the CCD causes overlap of a DC term and the desired correlation term in the output image. This overlap severely limits the correlation signal-to-noise ratio. The two-stage nature of the process limits the achievable throughput rate. A further limit is imposed by the low frame rate (typical video rates) of low- and medium-cost commercial CCDs.

Chao, Tien-Hsin↗

Evaluation of binary-phase-only filters for distortion-invariant pattern recognition

The paper examines the performance of fSDF (filter synthetic discriminant function) binary-phase-only filters (BPOFs) for images in the distortion-range that were not members of the training set. Consideration is given to the nature of the tradeoff between the extent of the distortion range and the correlator performance for fSDF-BPOFs, and to the probable constraints this tradeoff will impose on a realized optical correlator.

Jared, David A.↗

Computation and parallel implementation for early vision

The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.

Gualtieri, J. Anthony↗

Metric Learning to Enhance Hyperspectral Image Segmentation

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Thompson, David R.↗

Coarse-coded higher-order neural networks for PSRI object recognition

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

Spirkovska, Lilly↗

Interactive display/graphics systems for remote sensor data analysis

A color-television display system and interactive graphics equipment on-line to an IBM 360/44 computer are used to develop a variety of interactive displays which aid in analyzing remote sensor data. These interactive displays are used to: (1) analyze data from a multispectral scanner; (2) develop automatic pattern recognition systems based on multispectral scanner measurements; and (3) analyze data from non-imaging sensors such as the infrared radiometer and microwave scatterometer.

Eppler, W. G.↗

Introduction to computer image processing

Theoretical backgrounds and digital techniques for a class of image processing problems are presented. Image formation in the context of linear system theory, image evaluation, noise characteristics, mathematical operations on image and their implementation are discussed. Various techniques for image restoration and image enhancement are presented. Methods for object extraction and the problem of pictorial pattern recognition and classification are discussed.

Moik, J. G.↗