Results for “Object Recognition” — Page 6

NASA NTRS2021

AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars

Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1

Ono, Hiro↗

NASA NTRSDate not supplied

Early And Late Neurobehavioral Effects Of Male And Female Mice Exposed To Five-Ion GCRSim

With upcoming missions to the Moon and beyond to Mars, it is increasingly imperative to elucidate the detrimental effects of space travel beyond the lower Earth orbit, particularly to galactic cosmic radiation. Additionally, with the first female astronaut to soon travel to the Moon there is a strong need to understand the biological sex differences to adaptation to the deep space environment. While the effects of spaceflight on the nervous system are not fully known, studies in animal models have shown that exposure to ionizing radiation can cause neuronal damage and lead to downstream cognitive and behavioral deficits. Here, we investigated the neurobehavioral responses to space environment-like radiation exposure. Male and female 23–24-week-old mice (age-matched to average astronaut age) were exposed to 5, 15 and 50 cGy via Five-Ion Galactic Cosmic Ray Simulation at the NASA National Space Radiation Laboratory at Brookhaven National Laboratory. Both early (72hrs post exposure) and late (1-4 months post exposure) cognitive and behavioral deficits were investigated. Early analysis was performed by observing in-cage behavior including frequency/duration of digging, rearing, and grooming and nestlet building. Additionally, late effects were analyzed at NASA Ames via in-cage behavior as well as Catwalk (gait), Zero Maze (anxiety), Adhesive Removal (sensory motor), Novel Object Recognition and Barnes Maze (working memory). There were pronounced sex differences in both early and late time points. Females scored better than males in nestlet building, gait and working memory while males scored better at the sensory motor (adhesive removal) test. Dose effects were primarily observed at 50 cGy for both sexes, particularly with the Barnes Maze (working memory), where both males and females exposed to 50 cGy showed little to no improvement in test performance, though females again performed better than males. Currently, we are investigating the correlations between the observed behavior and cytokine levels in the plasma and brain. Future studies will continue to investigate cognitive consequences of galactic cosmic radiation in combination with other space-like environment stressors, including antigravity and social versus single housing.

S Puukila↗

NASA NTRSDate not supplied

Neurobehavioral Effects Of Five-Ion GCRSim Exposure In Male And Female Mice

Exposure to space galactic cosmic radiation is a principal consideration of spaceflight missions, and with upcoming missions to the Moon and Mars, it is increasingly imperative to elucidate the detrimental effects of space travel beyond the lower Earth orbit. Additionally, with the first female astronaut to soon travel to the Moon and beyond lower Earth orbit, there is a strong need to understand the biological sex differences to adaptation to the deep space environment. While the effects of spaceflight on the nervous system are not fully known, studies in animal models have shown that exposure to ionizing radiation can cause neuronal damage and lead to downstream cognitive and behavioral deficits. Therefore, we investigated the neurobehavioral responses to space environment-like radiation exposure. Male and female 23–24-week-old mice (age-matched to average astronaut age) were exposed to 5, 15 and 50 cGy via Five-Ion Galactic Cosmic Ray Simulation at the NASA National Space Radiation Laboratory at Brookhaven National Laboratory. Both early (72hrs post exposure) and late (1-4 months post exposure) cognitive and behavioral deficits were investigated. In-cage behavior was analyzed as frequency/duration of digging, rearing, and grooming and nestlet building using a 5-stage Deacon score. Additionally, at NASA Ames, behavior tests included Catwalk (gait), Zero Maze (anxiety), Adhesive Removal (sensory motor), Novel Object Recognition and Barnes Maze (working memory). There were pronounced sex differences in both early and late time points. Females typically performed the tests better than males, specifically in nestlet building, gait and working memory, though males performed better at the sensory motor (adhesive removal) test. Dose effects were primarily observed at 50 cGy for both sexes, particularly with the Barnes Maze (working memory), where both males and females exposed to 50 cGy showed little to no improvement in test performance, though females again performed better than males. Currently, we are investigating the correlations between the observed behavior and cytokine levels in the plasma and brain. Future studies will continue to investigate cognitive consequences of galactic cosmic radiation in combination with other space-like environment stressors, including antigravity and social versus single housing.

S. Puukila↗

NASA NTRSDate not supplied

Investigation of Geomorphological Signatures of Permafrost in the Polar Lunar Areas With VIPER

A study of images of the polar regions shows that small craters near the poles of the Moon are distinguished by the following features: 1. they have a smoother shape - see Fig (a); 2. patterned ground (“wrinkled skin”) is often observed in and around the crater - see Figs (a,b,c); 3. outside the craters landslides and cracks are noticeable - see Fig (d); 4. layers or scarps are often visible on the inner slope - see Fig (e). These features (scarps and patterned ground) are typical for Martian craters in the permafrost zone (see Fig f), as well as for similar zones on Earth. It is hypothesized that these features of lunar craters are associated with the presence of permafrost in the polar regions of the Moon. In 2023, the VIPER rover will investigate the distribution of ice (volatile) deposits in the region of the Moon's South Pole. VIPER navigation cameras represent a unique opportunity for the geomorphological analysis of the lunar surface and the study of physical properties of regolith due to multiple key factors: - A large number of high-quality digital images with a good resolution of the South Pole of the Moon, which is a key region for the landing of manned expeditions. - A low position of the Sun, which generates long shadows, creates favorable conditions for object recognition algorithms. - A presence of a rover track in the images enables VIPER wheels to be used as tools for the study of the regolith and the development of a geotechnical model of regolith in the South Pole region. Rover navigation cameras will allow investigation of the distribution and shape of small craters and other structures along the path of the rover and test the hypothesis about geomorphological signatures of permafrost in the lunar polar areas. If a relationship between characteristics of lunar craters and the distribution of permafrost is confirmed, this will open a possibility to remotely determine the deposits of lunar ice from satellite imagery.

VIPER↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRS2017

Object and Facial Recognition in Augmented and Virtual Reality: Investigation into Software, Hardware and Potential Uses

As augmented and virtual reality grows in popularity, and more researchers focus on its development, other fields of technology have grown in the hopes of integrating with the up-and-coming hardware currently on the market. Namely, there has been a focus on how to make an intuitive, hands-free human-computer interaction (HCI) utilizing AR and VR that allows users to control their technology with little to no physical interaction with hardware. Computer vision, which is utilized in devices such as the Microsoft Kinect, webcams and other similar hardware has shown potential in assisting with the development of a HCI system that requires next to no human interaction with computing hardware and software. Object and facial recognition are two subsets of computer vision, both of which can be applied to HCI systems in the fields of medicine, security, industrial development and other similar areas.

object recognition↗

NASA NTRS1991

Syntactic error modeling and scoring normalization in speech recognition

The objective was to develop the speech recognition system to be able to detect speech which is pronounced incorrectly, given that the text of the spoken speech is known to the recognizer. Research was performed in the following areas: (1) syntactic error modeling; (2) score normalization; and (3) phoneme error modeling. The study into the types of errors that a reader makes will provide the basis for creating tests which will approximate the use of the system in the real world. NASA-Johnson will develop this technology into a 'Literacy Tutor' in order to bring innovative concepts to the task of teaching adults to read.

Olorenshaw, Lex↗

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 NTRS1981

An algorithm for recognition and localization of rotated and scaled objects

An algorithm for recognition and localization of objects, which is invariant to displacement and rotation, is extended to the recognition and localization of differently scaled, rotated, and displaced objects. The proposed algorithm provides an optimum way to find if a match exists between two objects that are scaled, rotated, and displaced, while the number of computations is of the same order as for equally scaled objects.

Peli, T.↗

NASA NTRS1992

Recognition of partially occluded threat objects using the annealed Hopefield network

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

Kim, Jung H.↗

NASA NTRS1990

An optimal sensing strategy of a proximity sensor system for recognition and localization of polyhedral objects

An algorithm is presented for the recognition and localization of thre-dimensional polyhedral objects based on an optical proximity sensor system capable of measuring the depth and orientation of a local area of an object surface. Emphasis is given to the determination of an optimal sensor trajectory or an optimal probing, for efficient discrimination among all the possible interpretations. The determination of an optimal sensor trajectory for the next probing consists of the selection of optimal beam orientations based on the surface normal vector distribution of the multiple interpretation image (MII) and the selection of an optimal probing plane by projecting the MII onto the projection plane perpendicular to a selected beam orientation and deriving the optimal path on the projection plane. The selection of optimal beam orientation and probing plane is based on the measure of discrimination power of a cluster of surfaces of an MII. Simulation results are shown.

Lee, Sukhan↗

NASA NTRS1987

Multiple degree of freedom optical pattern recognition

Three general optical approaches to multiple degree of freedom object pattern recognition (where no stable object rest position exists) are advanced. These techniques include: feature extraction, correlation, and artificial intelligence. The details of the various processors are advanced together with initial results.

Casasent, D.↗

NASA NTRS1990

Recognition of 3-D symmetric objects from range images in automated assembly tasks

A new technique is presented for the three dimensional recognition of symmetric objects from range images. Beginning from the implicit representation of quadrics, a set of ten coefficients is determined for symmetric objects like spheres, cones, cylinders, ellipsoids, and parallelepipeds. Instead of using these ten coefficients trying to fit them to smooth surfaces (patches) based on the traditional way of determining curvatures, a new approach based on two dimensional geometry is used. For each symmetric object, a unique set of two dimensional curves is obtained from the various angles at which the object is intersected with a plane. Using the same ten coefficients obtained earlier and based on the discriminant method, each of these curves is classified as a parabola, circle, ellipse, or hyperbola. Each symmetric object is found to possess a unique set of these two dimensional curves whereby it can be differentiated from the others. It is shown that instead of using the three dimensional discriminant which involves evaluation of the rank of its matrix, it is sufficient to use the two dimensional discriminant which only requires three arithmetic operations.

Alvertos, Nicolas↗

NASA NTRS1968

Research on the utilization of pattern recognition techniques to identify and classify objects in video data Final report

Spaceborne pattern recognition system for identifying and classifying objects in video data

Source record↗

NASA NTRS1977

Recognition of compact astrophysical objects

NASA's Laboratory for High Energy Astrophysics and the Dept. of Physics and Astrophysics at the Univ. of Md. collaberated on a graduate level course with this title. This publication is an edited version of notes used as the course text. Topics include stellar evolution, pulsars, binary stars, X-ray signatures, gamma ray sources, and temporal analysis of X-ray data.

Ogelman, H.↗

NASA NTRS1988

Optical Recognition And Tracking Of Objects

Separate objects moving independently tracked simultaneously. System uses coherent optical techniques to obtain correlation between each object and reference image. Moving objects monitored by charge-coupled-device television camera, output fed to liquid-crystal television (LCTV) display. Acting as spatial light modulator, LCTV impresses images of moving objects on collimated laser beam. Beam spatially low-pass filtered to remove high-spatial-frequency television grid pattern.

Chao, Tien-Hsin↗

NASA NTRS1991

Analysis of objects in binary images

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Leonard, Desiree M.↗