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NASA NTRS1994

Dual use of image based tracking techniques: Laser eye surgery and low vision prosthesis

With a concentration on Fourier optics pattern recognition, we have developed several methods of tracking objects in dynamic imagery to automate certain space applications such as orbital rendezvous and spacecraft capture, or planetary landing. We are developing two of these techniques for Earth applications in real-time medical image processing. The first is warping of a video image, developed to evoke shift invariance to scale and rotation in correlation pattern recognition. The technology is being applied to compensation for certain field defects in low vision humans. The second is using the optical joint Fourier transform to track the translation of unmodeled scenes. Developed as an image fixation tool to assist in calculating shape from motion, it is being applied to tracking motions of the eyeball quickly enough to keep a laser photocoagulation spot fixed on the retina, thus avoiding collateral damage.

Juday, Richard D.↗

NASA NTRS1994

Dual Use of Image Based Tracking Techniques: Laser Eye Surgery and Low Vision Prosthesis

Juday, Richard D.↗

NASA NTRS2018

Integration of Optical Coherence Tomography Scan Patterns to Augment Clinical Data Suite

Vision changes identified in long duration spaceflight astronauts has led Space Medicine at NASA to adopt a more comprehensive clinical monitoring protocol. Optical Coherence Tomography (OCT) was recently implemented at NASA, including on board the International Space Station in 2013. NASA is collaborating with Heidelberg Engineering to increase the fidelity of the current OCT data set by integrating the traditional circumpapillary OCT image with radial and horizontal block images at the optic nerve head. The retinal nerve fiber layer was segmented by two experienced individuals. Intra-rater (N=4 subjects and 70 images) and inter-rater (N=4 subjects and 221 images) agreement was performed. The results of this analysis and the potential benefits will be presented.

Mason, S.↗

NASA NTRS2004

Development of an Intelligent Videogrammetric Wind Tunnel Measurement System

A videogrammetric technique developed at NASA Langley Research Center has been used at five NASA facilities at the Langley and Ames Research Centers for deformation measurements on a number of sting mounted and semispan models. These include high-speed research and transport models tested over a wide range of aerodynamic conditions including subsonic, transonic, and supersonic regimes. The technique, based on digital photogrammetry, has been used to measure model attitude, deformation, and sting bending. In addition, the technique has been used to study model injection rate effects and to calibrate and validate methods for predicting static aeroelastic deformations of wind tunnel models. An effort is currently underway to develop an intelligent videogrammetric measurement system that will be both useful and usable in large production wind tunnels while providing accurate data in a robust and timely manner. Designed to encode a higher degree of knowledge through computer vision, the system features advanced pattern recognition techniques to improve automated location and identification of targets placed on the wind tunnel model to be used for aerodynamic measurements such as attitude and deformation. This paper will describe the development and strategy of the new intelligent system that was used in a recent test at a large transonic wind tunnel.

Graves, Sharon S.↗

NASA NTRS1994

Microwave vision for robots

Microwave Vision (MV), a concept originally developed in 1985, could play a significant role in the solution to robotic vision problems. Originally our Microwave Vision concept was based on a pattern matching approach employing computer based stored replica correlation processing. Artificial Neural Network (ANN) processor technology offers an attractive alternative to the correlation processing approach, namely the ability to learn and to adapt to changing environments. This paper describes the Microwave Vision concept, some initial ANN-MV experiments, and the design of an ANN-MV system that has led to a second patent disclosure in the robotic vision field.

Lewandowski, Leon↗

NASA NTRS1982

An overview of computer vision

An overview of computer vision is provided. Image understanding and scene analysis are emphasized, and pertinent aspects of pattern recognition are treated. The basic approach to computer vision systems, the techniques utilized, applications, the current existing systems and state-of-the-art issues and research requirements, who is doing it and who is funding it, and future trends and expectations are reviewed.

Gevarter, W. B.↗

NASA NTRS2019

Distinct Pattern of Oculomotor Impairment Associated with Acute Sleep Loss and Circadian Misalignment

Sleep loss and circadianmisalignment have long been known to impair human cognitive and motor performance with significant societal and health consequences. It is well known that human reaction time to a visual cue is impaired following sleep loss and circadian misalignment, but it has remained unclear how more complex visuomotor control behaviour is altered under these conditions. In this study, we measured 14 parameters of the voluntary ocular tracking response of 12 human participants (six females) to systematically examine the effects of sleep loss and circadianmisalignment using a constant routine 24 h acute sleep-deprivation paradigm. The combination of state-of-the-art oculometric and sleep-research methodologies allowed us to document, for the first time, large changes in many components of pursuit, saccades and visual motion processing as a function of time awake and circadian phase. Further, we observed a pattern of impairment across our set of oculometric measures that is qualitatively different from that observed previously with other mild neural impairments. We conclude that dynamic vision and visuomotor control exhibit a distinct pattern of impairment linked with time awake and circadian phase. Therefore, a sufficiently broad set of oculometric measures could provide a sensitive and specific behavioural biomarker of acute sleep loss and circadian misalignment. We foresee potential applications of such oculometric biomarkers assisting in the assessment of readiness-to-perform higher risk tasks and in the characterization of sub-clinical neural impairment in the face of a multiplicity of potential risk factors, including disrupted sleep and circadian rhythms.

Stone, Leland S.↗

NASA NTRS2013

Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

Landsat satellite imagery was analyzed to generate a detailed record of 10 years of vegetation disturbance and regrowth for Pacific coastal areas of Marin and San Francisco Counties. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology, a transformation of Tasseled-Cap data space, was applied to detected changes in perennial coastal shrubland, woodland, and forest cover from 1999 to 2009. Results showed several principal points of interest, within which extensive contiguous areas of similar LEDAPS vegetation change (either disturbed or restored) were detected. Regrowth areas were delineated as burned forest areas in the Point Reyes National Seashore (PRNS) from the 1995 Vision Fire. LEDAPS-detected disturbance patterns on Inverness Ridge, PRNS in areas observed with dieback of tanoak and bay laurel trees was consistent with defoliation by sudden oak death (Phytophthora ramorum). LEDAPS regrowth pixels were detected over much of the predominantly grassland/herbaceous cover of the Olema Valley ranchland near PRNS. Extensive restoration of perennial vegetation cover on Crissy Field, Baker Beach and Lobos Creek dunes in San Francisco was identified. Based on these examples, the LEDAPS methodology will be capable of fulfilling much of the need for continual, low-cost monitoring of emerging changes to coastal ecosystems.

Land Cover Change↗

NASA NTRS2008

Software Simulates Sight: Flat Panel Mura Detection

In the increasingly sophisticated world of high-definition flat screen monitors and television screens, image clarity and the elimination of distortion are paramount concerns. As the devices that reproduce images become more and more sophisticated, so do the technologies that verify their accuracy. By simulating the manner in which a human eye perceives and interprets a visual stimulus, NASA scientists have found ways to automatically and accurately test new monitors and displays. The Spatial Standard Observer (SSO) software metric, developed by Dr. Andrew B. Watson at Ames Research Center, measures visibility and defects in screens, displays, and interfaces. In the design of such a software tool, a central challenge is determining which aspects of visual function to include while accuracy and generality are important, relative simplicity of the software module is also a key virtue. Based on data collected in ModelFest, a large cooperative multi-lab project hosted by the Optical Society of America, the SSO simulates a simplified model of human spatial vision, operating on a pair of images that are viewed at a specific viewing distance with pixels having a known relation to luminance. The SSO measures the visibility of foveal spatial patterns, or the discriminability of two patterns, by incorporating only a few essential components of vision. These components include local contrast transformation, a contrast sensitivity function, local masking, and local pooling. By this construction, the SSO provides output in units of "just noticeable differences" (JND) a unit of measure based on the assumed smallest difference of sensory input detectable by a human being. Herein is the truly amazing ability of the SSO, while conventional methods can manipulate images, the SSO models human perception. This set of equations actually defines a mathematical way of working with an image that accurately reflects the way in which the human eye and mind behold a stimulus. The SSO is intended for a wide variety of applications, such as evaluating vision from unmanned aerial vehicles, measuring visibility of damage to aircraft and to the space shuttles, predicting outcomes of corrective laser eye surgery, inspecting displays during the manufacturing process, estimating the quality of compressed digital video, evaluating legibility of text, and predicting discriminability of icons or symbols in a graphical user interface.

Source record↗

NASA NTRS1984

Image-plane processing of visual information

Shannon's theory of information is used to optimize the optical design of sensor-array imaging systems which use neighborhood image-plane signal processing for enhancing edges and compressing dynamic range during image formation. The resultant edge-enhancement, or band-pass-filter, response is found to be very similar to that of human vision. Comparisons of traits in human vision with results from information theory suggest that: (1) Image-plane processing, like preprocessing in human vision, can improve visual information acquisition for pattern recognition when resolving power, sensitivity, and dynamic range are constrained. Improvements include reduced sensitivity to changes in lighter levels, reduced signal dynamic range, reduced data transmission and processing, and reduced aliasing and photosensor noise degradation. (2) Information content can be an appropriate figure of merit for optimizing the optical design of imaging systems when visual information is acquired for pattern recognition. The design trade-offs involve spatial response, sensitivity, and sampling interval.

Huck, F. O.↗

NASA NTRS1990

Multiscale vector fields for image pattern recognition

A uniform processing framework for low-level vision computing in which a bank of spatial filters maps the image intensity structure at each pixel into an abstract feature space is proposed. Some properties of the filters and the feature space are described. Local orientation is measured by a vector sum in the feature space as follows: each filter's preferred orientation along with the strength of the filter's output determine the orientation and the length of a vector in the feature space; the vectors for all filters are summed to yield a resultant vector for a particular pixel and scale. The orientation of the resultant vector indicates the local orientation, and the magnitude of the vector indicates the strength of the local orientation preference. Limitations of the vector sum method are discussed. Investigations show that the processing framework provides a useful, redundant representation of image structure across orientation and scale.

Low, Kah-Chan↗

NASA NTRS1992

NASA in the 21st century: A vision of greatness

Notions of greatness are discussed that have guided NASA in the past, values are presented that might be delivered by NASA in the future, and the the skills required for NASA to execute a vision of greatness are examined. Three possible patterns of space development by NASA are reviewed: (1) a mission to protect the ecology of the Earth; (2) the engineering of the technologies critical to space transportation and a healthy, productive life in space; and (3) the management of a major nonterrestrial resource project. Potential sources of funds are discussed along with opportunities for sustainable collaboration, and the life cycle of NASA's funding responsibility for its space development program.

Murphy, Kathleen J.↗

NASA NTRS1996

Active-Pixel Image Sensors With Programmable Resolution

Active-pixel image sensors with programmable resolution proposed for use in applications in which speed and efficiency of processing of image data enhanced by providing those data at varying resolutions. Such applications include modeling of biological vision, stereoscopic range-finding, recognition of patterns, tracking targets, and progressive transmission of compressed images. In target-tracking application, sensor initially forms low-resolution image from which area of interest identified, then sensor set at high resolution for examination of identified area. Outputs of contiguous pixels combined. Sensor of this type made to act as though it comprised fewer and larger pixels.

Kemeny, Sabrina E.↗

NASA NTRS2015

Anomalous Cases of Astronaut Helmet Detection

An astronaut's helmet is an invariant, rigid image element that is well suited for identification and tracking using current machine vision technology. Future space exploration will benefit from the development of astronaut detection software for search and rescue missions based on EVA helmet identification. However, helmets are solid white, except for metal brackets to attach accessories such as supplementary lights. We compared the performance of a widely used machine vision pipeline on a standard-issue NASA helmet with and without affixed experimental feature-rich patterns. Performance on the patterned helmet was far more robust. We found that four different feature-rich patterns are sufficient to identify a helmet and determine orientation as it is rotated about the yaw, pitch, and roll axes. During helmet rotation the field of view changes to frames containing parts of two or more feature-rich patterns. We took reference images in these locations to fill in detection gaps. These multiple feature-rich patterns references added substantial benefit to detection, however, they generated the majority of the anomalous cases. In these few instances, our algorithm keys in on one feature-rich pattern of the multiple feature-rich pattern reference and makes an incorrect prediction of the location of the other feature-rich patterns. We describe and make recommendations on ways to mitigate anomalous cases in which detection of one or more feature-rich patterns fails. While the number of cases is only a small percentage of the tested helmet orientations, they illustrate important design considerations for future spacesuits. In addition to our four successful feature-rich patterns, we present unsuccessful patterns and discuss the cause of their poor performance from a machine vision perspective. Future helmets designed with these considerations will enable automated astronaut detection and thereby enhance mission operations and extraterrestrial search and rescue.

Dolph, Chester↗

NASA NTRS1991

Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data

The overall aim is to provide base technology for an automated vision system for on-board interpretation of geophysical data. During the first year's work, it was demonstrated that geophysical data can be treated as patterns and interpreted using single neural networks. Current research is developing an integrated vision system comprising neural networks, algorithmic preprocessing, and expert knowledge. This system is to be tested incrementally using synthetic geophysical patterns, laboratory generated geophysical patterns, and field geophysical patterns.

Glass, Charles E.↗

NASA NTRS1973

Landing performance by low-time private pilots after the sudden loss of binocular vision - Cyclops II

Study of low-time general aviation pilots, who, in a series of spot landings, were suddenly deprived of binocular vision by patching either eye on the downwind leg of a standard, closed traffic pattern. Data collected during these landings were compared with control data from landings flown with normal vision during the same flight. The sequence of patching and the mix of control and monocular landings were randomized to minimize the effect of learning. No decrease in performance was observed during landings with vision restricted to one eye, in fact, performance improved. This observation is reported at a high level of confidence (p less than 0.001). These findings confirm the previous work of Lewis and Krier and have important implications with regard to aeromedical certification standards.

Lewis, C. E., Jr.↗

NASA NTRS1991

Higher-order neural network software for distortion invariant object recognition

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Reid, Max B.↗

NASA NTRS1997

Model of visual contrast gain control and pattern masking

We have implemented a model of contrast gain and control in human vision that incorporates a number of key features, including a contrast sensitivity function, multiple oriented bandpass channels, accelerating nonlinearities, and a devisive inhibitory gain control pool. The parameters of this model have been optimized through a fit to the recent data that describe masking of a Gabor function by cosine and Gabor masks [J. M. Foley, "Human luminance pattern mechanisms: masking experiments require a new model," J. Opt. Soc. Am. A 11, 1710 (1994)]. The model achieves a good fit to the data. We also demonstrate how the concept of recruitment may accommodate a variant of this model in which excitatory and inhibitory paths have a common accelerating nonlinearity, but which include multiple channels tuned to different levels of contrast.

NASA Discipline Space Human Factors↗