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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Computer vision for real-time orbital operations. Center directors discretionary fund

Machine vision research is examined as it relates to the NASA Space Station program and its associated Orbital Maneuvering Vehicle (OMV). Initial operation of OMV for orbital assembly, docking, and servicing are manually controlled from the ground by means of an on board TV camera. These orbital operations may be accomplished autonomously by machine vision techniques which use the TV camera as a sensing device. Classical machine vision techniques are described. An alternate method is developed and described which employs a syntactic pattern recognition scheme. It has the potential for substantial reduction of computing and data storage requirements in comparison to the Two-Dimensional Fast Fourier Transform (2D FFT) image analysis. The method embodies powerful heuristic pattern recognition capability by identifying image shapes such as elongation, symmetry, number of appendages, and the relative length of appendages.

Vinz, F. L.↗
NASA NTRS1995

A comparison of Image Quality Models and Metrics Predicting Object Detection

Many models and metrics for image quality predict image discriminability, the visibility of the difference between a pair of images. Some image quality applications, such as the quality of imaging radar displays, are concerned with object detection and recognition. Object detection involves looking for one of a large set of object sub-images in a large set of background images and has been approached from this general point of view. We find that discrimination models and metrics can predict the relative detectability of objects in different images, suggesting that these simpler models may be useful in some object detection and recognition applications. Here we compare three alternative measures of image discrimination, a multiple frequency channel model, a single filter model, and RMS error.

Rohaly, Ann Marie↗
NASA NTRS1993

Robust algebraic image enhancement for intelligent control systems

Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

Lerner, Bao-Ting↗
NASA NTRS1990

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Hepner, George F.↗
NASA NTRS1991

Vision-guided gripping of a cylinder

The motivation for vision-guided servoing is taken from tasks in automated or telerobotic space assembly and construction. Vision-guided servoing requires the ability to perform rapid pose estimates and provide predictive feature tracking. Monocular information from a gripper-mounted camera is used to servo the gripper to grasp a cylinder. The procedure is divided into recognition and servo phases. The recognition stage verifies the presence of a cylinder in the camera field of view. Then an initial pose estimate is computed and uncluttered scan regions are selected. The servo phase processes only the selected scan regions of the image. Given the knowledge, from the recognition phase, that there is a cylinder in the image and knowing the radius of the cylinder, 4 of the 6 pose parameters can be estimated with minimal computation. The relative motion of the cylinder is obtained by using the current pose and prior pose estimates. The motion information is then used to generate a predictive feature-based trajectory for the path of the gripper.

Nicewarner, Keith E.↗
NASA NTRS1972

Optical computing.

Applications of the optical computer include an approach for increasing the sharpness of images obtained from the most powerful electron microscopes and fingerprint/credit card identification. The information-handling capability of the various optical computing processes is very great. Modern synthetic-aperture radars scan upward of 100,000 resolvable elements per second. Fields which have assumed major importance on the basis of optical computing principles are optical image deblurring, coherent side-looking synthetic-aperture radar, and correlative pattern recognition. Some examples of the most dramatic image deblurring results are shown.

Stroke, G. W.↗
DOE OSTI2020

LIvermore SEM image Tools

Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information for the characterization of the nanomaterials. This code offers automatic recognition and quantitative analysis of SEM images in a high-throughput manner using computer vision and machine learning techniques. The main function of this application is to extract particle size and morphology information of overlapping nanoparticles and core-shell nanostructures in a user friendly interface. The code is written in C++ with QT environment, and has been tested on MacOSX.

KIM, HYOJIN↗
DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗
NASA NTRS2019

Space Communications and Navigation Validation: Extracting Data for the Strategic Center for Networking, Integration, and Communications Scheduling Algorithms

Efficiency in communication system architecture performance between Space Communications and Navigation (SCaN) assets and missions is crucial, as space communication is varied, complex, and often not utilized to its full potential. The SCaN Strategic Center for Networking, Integration, and Communications (SCENIC) new scheduling algorithms, which are designed to simulate the allocation of resources between SCaN assets and missions, have the potential to simulate an increase of this efficiency; however, they require real-world data to be validated against. The purpose of this project was to extract said validation data, which details the frequency and duration of utilized contact windows between missions and assets in the Near Earth Network (NEN), Space Network (SN), and Deep Space Network (DSN). Stored as images in daily operations summaries (DOSs), the tabular data existed in a variety of file formats such as.pdf, .docx, and .doc. Since the tables were stored as images, ABBYY® FineReader® (ABBYY Software Ltd.) optical character recognition (OCR) was implemented, which is a proprietary software that reads images from text. The comma separated value (CSV) output was utilized as input to a series of MATLAB® (The MathWorks, Inc.) methods for reformatting, at which point it was ready to be machine-read. Finally, the results were converted to a Microsoft Excel format for human readability. Along with being used for validation purposes, the data will also be used to map equipment degradation as a function of time to analyze the reliability of network assets.

Kontur, Noah P.↗
NASA NTRS1999

Tracker: Image-Processing and Object-Tracking System Developed

Tracker is an object-tracking and image-processing program designed and developed at the NASA Lewis Research Center to help with the analysis of images generated by microgravity combustion and fluid physics experiments. Experiments are often recorded on film or videotape for analysis later. Tracker automates the process of examining each frame of the recorded experiment, performing image-processing operations to bring out the desired detail, and recording the positions of the objects of interest. It can load sequences of images from disk files or acquire images (via a frame grabber) from film transports, videotape, laser disks, or a live camera. Tracker controls the image source to automatically advance to the next frame. It can employ a large array of image-processing operations to enhance the detail of the acquired images and can analyze an arbitrarily large number of objects simultaneously. Several different tracking algorithms are available, including conventional threshold and correlation-based techniques, and more esoteric procedures such as "snake" tracking and automated recognition of character data in the image. The Tracker software was written to be operated by researchers, thus every attempt was made to make the software as user friendly and self-explanatory as possible. Tracker is used by most of the microgravity combustion and fluid physics experiments performed by Lewis, and by visiting researchers. This includes experiments performed on the space shuttles, Mir, sounding rockets, zero-g research airplanes, drop towers, and ground-based laboratories. This software automates the analysis of the flame or liquid s physical parameters such as position, velocity, acceleration, size, shape, intensity characteristics, color, and centroid, as well as a number of other measurements. It can perform these operations on multiple objects simultaneously. Another key feature of Tracker is that it performs optical character recognition (OCR). This feature is useful in extracting numerical instrumentation data that are embedded in images. All the results are saved in files for further data reduction and graphing. There are currently three Tracking Systems (workstations) operating near the laboratories and offices of Lewis Microgravity Science Division researchers. These systems are used independently by students, scientists, and university-based principal investigators. The researchers bring their tapes or films to the workstation and perform the tracking analysis. The resultant data files generated by the tracking process can then be analyzed on the spot, although most of the time researchers prefer to transfer them via the network to their offices for further analysis or plotting. In addition, many researchers have installed Tracker on computers in their office for desktop analysis of digital image sequences, which can be digitized by the Tracking System or some other means. Tracker has not only provided a capability to efficiently and automatically analyze large volumes of data, saving many hours of tedious work, but has also provided new capabilities to extract valuable information and phenomena that was heretofore undetected and unexploited.

Klimek, Robert B.↗
NASA NTRS2011

Image Analysis via Soft Computing: Prototype Applications at NASA KSC and Product Commercialization

This slide presentation reviews the use of "soft computing" which differs from "hard computing" in that it is more tolerant of imprecision, partial truth, uncertainty, and approximation and its use in image analysis. Soft computing provides flexible information processing to handle real life ambiguous situations and achieve tractability, robustness low solution cost, and a closer resemblance to human decision making. Several systems are or have been developed: Fuzzy Reasoning Edge Detection (FRED), Fuzzy Reasoning Adaptive Thresholding (FRAT), Image enhancement techniques, and visual/pattern recognition. These systems are compared with examples that show the effectiveness of each. NASA applications that are reviewed are: Real-Time (RT) Anomaly Detection, Real-Time (RT) Moving Debris Detection and the Columbia Investigation. The RT anomaly detection reviewed the case of a damaged cable for the emergency egress system. The use of these techniques is further illustrated in the Columbia investigation with the location and detection of Foam debris. There are several applications in commercial usage: image enhancement, human screening and privacy protection, visual inspection, 3D heart visualization, tumor detections and x ray image enhancement.

Dominguez, Jesus A.↗
NASA NTRS2015

Double-Diffusive Convection During Growth of Halides and Selenides

Heavy metal halides and selenides have unique properties which make them excellent materials for chemical, biological and radiological sensors. Recently it has been shown that selenohalides are even better materials than halides or selenides for gamma-ray detection. These materials also meet the strong needs of a wide band imaging technology to cover ultra-violet (UV), midwave infrared wavelength (MWIR) to very long wavelength infrared (VLWIR) region for hyperspectral imager components such as etalon filters and acousto-optic tunable filters (AO). In fact AOTF based imagers based on these materials have some superiority than imagers based on liquid crystals, FTIR, Fabry-Perot, grating, etalon, electro-optic modulation, piezoelectric and several other concepts. For example, broadband spectral and imagers have problems of processing large amount of information during real-time observation. Acousto-Optic Tunable Filter (AOTF) imagers are being developed to fill the need of reducing processing time of data, low cost operation and key to achieving the goal of covering long-wave infrared (LWIR). At the present time spectral imaging systems are based on the use of diffraction gratings are typically used in a pushbroom or whiskbroom mode. They are mostly used in systems and acquire large amounts of hyperspectral data that is processed off-line later. In contrast, acousto-optic tunable filter spectral imagers require very little image processing, providing new strategies for object recognition and tracking. They are ideally suited for tactical situations requiring immediate real-time image processing. But the performance of these imagers depends on the quality and homogeneity of acousto-optic materials. In addition for many systems requirements are so demanding that crystals up to sizes of 10 cm length are desired. We have studied several selenides and halide crystals for laser and AO imagers for MWIR and LWIR wavelength regions. We have grown and fabricated crystals of several materials such as mercurous chloride, mercurous bromide, mercurous iodide, lead chloride lead bromide, lead iodide, thallium arsenic selenide, gallium selenide, zince sulfide zinc selenide and several crystals into devices. We have used both Bridgman and physical vapor transport (PVT) crystal growth methods. In the past have examined PVT growth numerically for conditions where the boundary of the enclosure is subjected to a nonlinear thermal profile. Since past few months we have been working on binary and ternary materials such as selenoiodides, doped zinc sulfides and mercurous chloro bromide and mercurous bromoiodides. In the doped and ternary materials thermal and solutal convection play extremely important role during the growth. Very commonly striations and banding is observed. Our experiments have indicated that even in highly purified source materials, homogeneity in 1-g environment is very difficult. Some of our previous numerical studies have indicated that gravity level less than 10-4 (-g) helps in controlling the thermosolutal convection. We will discuss the ground based growth results of HgClxBr(1-x) and ZnSe growth results for the mm thick to large cm size crystals. These results will be compared with our microgravity experiments performed with this class of materials. For both HgCl-HgBr and ZnS-ZnSe the lattice parameters of the mixtures obey Vagard's law in the studied composition range. The study demonstrates that properties are very anisotropic with crystal orientation, and performance achievement requires extremely careful fabrication to utilize highest figure of merit. In addition, some parameters such as crystal growth fabrication, processing time, resolution, field of view and efficiency will be described based on novel solid solution materials. It was predicted that very similar to the pure compounds solid solutions also have very large anisotropy, and very precise oriented and homogeneous bulk and thin film crystals is required to achieve maximum performance of laser or imagers. Some of the parameters controlling the homogeneity such as thermos-solutal convection driven forces can be controlled in microgravity environments to utilize the benefits of these unique materials.

Singh, N. B.↗
NASA NTRS1992

Global stratigraphy

Attention is given to recent major advances in the definition and documentation of Martian stratigraphy and geology. Mariner 9 provided the images for the first global geologic mapping program, resulting in the recognition of the major geologic processes that have operated on the planet, and in the definition of the three major chronostratigraphic divisions: the Noachian, Hesperian, and Amazonian Systems. Viking Orbiter images permitted the recognition of additional geologic units and the formal naming of many formations. Epochs are assigned absolute ages based on the densities of superposed craters and crater-flux models. Recommendations are made with regard to future areas of study, namely, crustal stratigraphy and structure, the highland-lowland boundary, the Tharsis Rise, Valles Marineris, channels and valley networks, and possible Martian oceans, lakes, and ponds.

Tanaka, Kenneth L.↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS1994

Optimal Correlation Filters for Images with Signal-Dependent Noise

We address the design of optimal correlation filters for pattern detection and recognition in the presence of signal-dependent image noise sources. The particular examples considered are film-grain noise and speckle. Two basic approaches are investigated: (1) deriving the optimal matched filters for the signal-dependent noise models and comparing their performances with those derived for traditional signal-independent noise models and (2) first nonlinearly transforming the signal-dependent noise to signal-independent noise followed by the use of a classical filter matched to the transformed signal. We present both theoretical and computer simulation results that demonstrate the generally superior performance of the second approach in terms of the correlation peak signal-to-noise ratio.

Downie, John D.↗
NASA NTRS1987

Knowledge-based vision for space station object motion detection, recognition, and tracking

Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed.

Symosek, P.↗
NASA NTRS1977

Image analysis library software development

The Image Analysis Library consists of a collection of general purpose mathematical/statistical routines and special purpose data analysis/pattern recognition routines basic to the development of image analysis techniques for support of current and future Earth Resources Programs. Work was done to provide a collection of computer routines and associated documentation which form a part of the Image Analysis Library.

Guseman, L. F., Jr.↗