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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Brain segmentation and the generation of cortical surfaces

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

NASA Discipline Neuroscience↗
NASA NTRS2022

Test methods for image-based information in next-generation manufacturing

Typical control designs in the process systems engineering literature have assumed that the primary sensing methodologies are traditional instruments such as thermocouples. Digitalization is changing the landscape for manufacturing, and data-based sensing modalities (e.g., image-based sensing) are becoming of greater interest for plant control. These considerations require novel test/evaluation solutions. For example, process systems engineering researchers may wish to test image-based sensors in simulation. In this work, we provide preliminary thoughts on how image-based technologies might be evaluated via simulation for process systems.

Henrique Oyama↗
NASA NTRS1999

Multi Spectral Imaging System

An optical imaging system provides automatic co-registration of a plurality of multi spectral images of an object which are generated by a plurality of video cameras or other optical detectors. The imaging system includes a modular assembly of beam splitters, lens tubes, camera lenses and wavelength selective filters which facilitate easy reconfiguration and adjustment of the system for various applications. A primary lens assembly generates a real image of an object to be imaged on a reticle which is positioned at a fixed length from a beam splitter assembly. The beam splitter assembly separates a collimated image beam received from the reticle into multiple image beams, each of which is projected onto a corresponding one of a plurality of video cameras. The lens tubes which connect the beam splitter assembly to the cameras are adjustable in length to provide automatic co-registration of the images generated by each camera.

Spiering, Bruce A.↗
NASA NTRS1983

Ocular screening tests of elementary school children

This report presents an analysis of 507 abnormal retinal reflex images taken of Huntsville kindergarten and first grade students. The retinal reflex images were obtained by using an MSFC-developed Generated Retinal Reflex Image System (GRRIS) photorefractor. The system uses a 35 mm camera with a telephoto lens with an electronic flash attachment. Slide images of the eyes were examined for abnormalities. Of a total of 1835 students screened for ocular abnormalities, 507 were found to have abnormal retinal reflexes. The types of ocular abnormalities detected were hyperopia, myopia, astigmatism, esotropia, exotropia, strabismus, and lens obstuctions. The report shows that the use of the photorefractor screening system is an effective low-cost means of screening school children for abnormalities.

Richardson, J.↗
NASA NTRS1985

Estimating Doppler Shifts for Synthetic-Aperture Radar

Quality of synthetic-aperture-radar (SAR) imagery enhanced by pair of signal-processing techniques that compensate for relative motion between target and vehicle. Combining vehicle trajectory data with radar-parameter and raw image data, techniques generate Doppler estimates for use in constructing fully processed images.

Curlander, J. C.↗
NASA NTRS1989

Atmospheric autorotating imaging device

An imaging device that automatically rotates upon descent through an atmosphere provides an onboard image detector a sweeping panoramic scan as it descends. No moving parts or propulsion system are required. The location, angle and pitch of the winged structure, together with its inertia properties, passively induces rotation. The angled location of the image detector takes advantage of the resulting rotation. Data generated by the image detector may be transmitted to a remote receiver or, alternatively, stored for subsequent recovery.

Burke, James D.↗
NASA NTRS2017

Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.

Kateb, Babak↗
DOE OSTI2022

Optical authentication of images

Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optical authentication of images

Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

Murialdo, Maxwell R.↗
DOE OSTI2021

Multi-frame moving object detection system

A plurality of remote sensing images of a scene are received. A potential target object can be identified in one of the images, wherein the target object has a low signal-to-noise ratio (SNR). A candidate motion path of the target object can be generated based upon the images. A predicted position of the target object along the candidate motion path is determined for each of the remote sensing images. An image chip is extracted from each of the images, where each image chip is centered about the predicted position of the target object in its corresponding image. A sum image chip is generated based upon the image chips. An indication that the potential target object is an actual object in the images is output based upon a value of a center pixel of the sum image chip.

Ma, Tian J.↗
DOE OSTI2021

Identifying Genomic Islands with Deep Neural Networks

Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Computer Vision↗
NASA NTRS1998

Image enhancement by non-linear extrapolation in frequency space

An input image is enhanced to include spatial frequency components having frequencies higher than those in an input image. To this end, an edge map is generated from the input image using a high band pass filtering technique. An enhancing map is subsequently generated from the edge map, with the enhanced map having spatial frequencies exceeding an initial maximum spatial frequency of the input image. The enhanced map is generated by applying a non-linear operator to the edge map in a manner which preserves the phase transitions of the edges of the input image. The enhanced map is added to the input image to achieve a resulting image having spatial frequencies greater than those in the input image. Simplicity of computations and ease of implementation allow for image sharpening after enlargement and for real-time applications such as videophones, advanced definition television, zooming, and restoration of old motion pictures.

Anderson, Charles H.↗
DOE OSTI2022

Utilization of the Critic Subnetwork of a Generative Adversarial Network as Detector of Morphological Material Change in Image Data

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. For this work, we explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Hard X-ray Observation of Cygnus X-1 By the Marshall Imaging X-ray Experiment (MIXE2)

The second generation of the Marshall Imaging X-ray Experiment (MIXE2) was flown from Fort Sumner, New Mexico on May 7-8, 1997. The experiment consists of coded-aperture telescope with a field of view of 1.8 degrees (FWHM) and an angular resolution of 6.9 arcminutes. The detector is a large (7.84x10(exp 4) sq cm) effective area microstrip proportional counter filled with 2.0x10(exp5) Pascals of xenon with 2% isobutylene. We present MIXE2 observation of the 20-80keV spectrum and timing variability of Cygnus X-1 made during balloon flight.

Minamitani, Takahisa↗
NASA NTRS2004

Modeling Images of Natural 3D Surfaces: Overview and Potential Applications

Generative models of natural images have long been used in computer vision. However, since they only describe the of 2D scenes, they fail to capture all the properties of the underlying 3D world. Even though such models are sufficient for many vision tasks a 3D scene model is when it comes to inferring a 3D object or its characteristics. In this paper, we present such a generative model, incorporating both a multiscale surface prior model for surface geometry and reflectance, and an image formation process model based on realistic rendering, the computation of the posterior model parameter densities, and on the critical aspects of the rendering. We also how to efficiently invert the model within a Bayesian framework. We present a few potential applications, such as asteroid modeling and Planetary topography recovery, illustrated by promising results on real images.

Jalobeanu, Andre↗
NASA NTRS2006

Processing of Mars Exploration Rover Imagery for Science and Operations Planning

The twin Mars Exploration Rovers (MER) delivered an unprecedented array of image sensors to the Mars surface. These cameras were essential for operations, science, and public engagement. The Multimission Image Processing Laboratory (MIPL) at the Jet Propulsion Laboratory was responsible for the first-order processing of all of the images returned by these cameras. This processing included reconstruction of the original images, systematic and ad hoc generation of a wide variety of products derived from those images, and delivery of the data to a variety of customers, within tight time constraints. A combination of automated and manual processes was developed to meet these requirements, with significant inheritance from prior missions. This paper describes the image products generated by MIPL for MER and the processes used to produce and deliver them.

Instruments↗
DOE OSTI2023

Systems and methods for verification and authentication of remote sensing imagery

A system for generating and authenticating remote sensing imagery includes a remote sensing platform. The remote sensing platform includes an imaging sensor and a hardware logic device that is directly coupled to the imaging sensor. The hardware logic device receives image data from the imaging sensor and generates a cryptographic signature based upon the image data. The hardware logic device outputs a signed image that includes the image data and the cryptographic signature. A computing device operated by a downstream user receives the signed image and can determine whether the image data has been modified from its original content based upon the cryptographic signature.

Crosby, Sean Michael↗