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NASA NTRS2003

Software for Partly Automated Recognition of Targets

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Opitz, David↗

DOE OSTI2020

Insights into the Structure and Energy of DNA Nanoassemblies

Since the pioneering work of Ned Seeman in the early 1980s, the use of the DNA molecule as a construction material experienced a rapid growth and led to the establishment of a new field of science, nowadays called structural DNA nanotechnology. Here, the self-recognition properties of DNA are employed to build micrometer-large molecular objects with nanometer-sized features, thus bridging the nano- to the microscopic world in a programmable fashion. Distinct design strategies and experimental procedures have been developed over the years, enabling the realization of extremely sophisticated structures with a level of control that approaches that of natural macromolecular assemblies. Nevertheless, our understanding of the building process, i.e., what defines the route that goes from the initial mixture of DNA strands to the final intertwined superstructure, is, in some cases, still limited. In this review, we describe the main structural and energetic features of DNA nanoconstructs, from the simple Holliday junction to more complicated DNA architectures, and present the theoretical frameworks that have been formulated until now to explain their self-assembly. Deeper insights into the underlying principles of DNA self-assembly may certainly help us to overcome current experimental challenges and foster the development of original strategies inspired to dissipative and evolutive assembly processes occurring in nature.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1984

The Sternberg Task as a Workload Metric in Flight Handling Qualities Research

The objective of this research was to determine whether the Sternberg item-recognition task, employed as a secondary task measure of spare mental capacity for flight handling qualities (FHQ) simulation research, could help to differentiate between different flight-control conditions. FHQ evaluations were conducted on the Vertical Motion Simulator at Ames Research Center to investigate different primary flight-control configurations, and selected stability and control augmentation levels for helicopers engaged in low-level flight regimes. The Sternberg task was superimposed upon the primary flight-control task in a balanced experimental design. The results of parametric statistical analysis of Sternberg secondary task data failed to support the continued use of this task as a measure of pilot workload. In addition to the secondary task, subjects provided Cooper-Harper pilot ratings (CHPR) and responded to a workload questionnaire. The CHPR data also failed to provide reliable statistical discrimination between FHQ treatment conditions; some insight into the behavior of the secondary task was gained from the workload questionnaire data.

Hemingway, J. C.↗

NASA NTRS1989

An improved architecture for video rate image transformations

Geometric image transformations are of interest to pattern recognition algorithms for their use in simplifying some aspects of the pattern recognition process. Examples include reducing sensitivity to rotation, scale, and perspective of the object being recognized. The NASA Programmable Remapper can perform a wide variety of geometric transforms at full video rate. An architecture is proposed that extends its abilities and alleviates many of the first version's shortcomings. The need for the improvements are discussed in the context of the initial Programmable Remapper and the benefits and limitations it has delivered. The implementation and capabilities of the proposed architecture are discussed.

Fisher, Timothy E.↗

NASA NTRS1993

Optical processing for semiautonomous terminal navigation and docking

An optical cross correlator that recognizes a single object is suitable for performing a single-vision function, such as pattern recognition for semiautonomous navigation, landing, and docking of vehicles to a predesignated landing mark. The optical cross correlator, with a video input from a simple imaging system and the output of the optical correlation plane processed with standard star tracker software, produces sufficient information for a spacecraft's terminal homing navigation system to complete a docking maneuver. We describe the application of the optical cross correlator to a landing on a simulated space station.

Scholl, M. S.↗

NASA NTRS1978

Modular on-board adaptive imaging

Feature extraction involves the transformation of a raw video image to a more compact representation of the scene in which relevant information about objects of interest is retained. The task of the low-level processor is to extract object outlines and pass the data to the high-level process in a format that facilitates pattern recognition tasks. Due to the immense computational load caused by processing a 256x256 image, even a fast minicomputer requires a few seconds to complete this low-level processing. It is, therefore, necessary to consider hardware implementation of these low-level functions to achieve real-time processing speeds. The considered project had the objective to implement a system in which the continuous feature extraction process is not affected by the dynamic changes in the scene, varying lighting conditions, or object motion relative to the cameras. Due to the high bandwidth (3.5 MHz) and serial nature of the TV data, a pipeline processing scheme was adopted as the overall architecture of this system. Modularity in the system is achieved by designing circuits that are generic within the overall system.

Eskenazi, R.↗

NASA NTRSDate not supplied

Astrobiological Potential of Rocks Acquired By the Perseverance Rover at the Front of the Western Sediment Fan in Jezero Crater, Mars.

Major objectives of the Perseverance rover mission include identifying past habitable environments, collecting rocks that are likely to preserve biosignatures and using the rover’s instruments to look for potential biosignatures in these rocks. The recognition of the > 3.5 billion-year old habitable environments in Jezero crater, Mars (Fig. 1); promised that the mission could achieve these objectives. The western sedimentary fan deposit, the hydrated minerals therein and the carbonate minerals at the margin of this fan were identified as particularly compelling areas in which to search for the signs of past life and collect the oldest aqueously deposited rocks from another planet.

Mars 2020↗

NASA NTRS1986

Pattern recognition of satellite cloud imagery for improved weather prediction

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Gautier, Catherine↗

NASA NTRS1973

Recognition of beach and nearshore depositional features of Chesapeake Bay

Beach and nearshore depositional features are being mapped with the objectives of determining a quick-look analysis of littoral drift and sedimentation patterns in areas of little or no data. Evaluation of beach and nearshore features aid in the selection of small boat harbors, shoreline protective structures, and general coastal zone development. Through ERTS-1 aircraft support imagery, beach depositional features mapped are cuspate forelands, welded beach ridges, and recurved spits. The nearshore depositional features exhibit a bar and trough topography with three distinct types of sedimentary structures; longshore, transverse, and reticulated bars. Synoptic coverage of beach and nearshore depositional features by ERTS-1 data help in determining the general sedimentation patterns, growth of the beach features and stability of the bar and trough topography.

Kerhin, R. T.↗

NASA NTRS1992

Optical processing for range and attitude determination

A rugged, miniaturized, optical cross-correlator that recognizes a single object is particularly suitable for performing a single-vision function, such as pattern recognition for semi-autonomous navigation, landing, and docking of vehicles to a pre-designated landing mark. The optical cross-correlator, with a video input from a simple imaging system and the output of the optical correlation plane processed using the standard star tracker software, produces sufficient information for a spacecraft's terminal homing navigation system to complete a docking maneuver.

Scholl, M. S.↗

NASA NTRS2017

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗

NASA NTRS1985

Vision requirements for Space Station applications

Problems which will be encountered by computer vision systems in Space Station operations are discussed, along with solutions be examined at Johnson Space Station. Lighting cannot be controlled in space, nor can the random presence of reflective surfaces. Task-oriented capabilities are to include docking to moving objects, identification of unexpected objects during autonomous flights to different orbits, and diagnoses of damage and repair requirements for autonomous Space Station inspection robots. The approaches being examined to provide these and other capabilities are television IR sensors, advanced pattern recognition programs feeding on data from laser probes, laser radar for robot eyesight and arrays of SMART sensors for automated location and tracking of target objects. Attention is also being given to liquid crystal light valves for optical processing of images for comparisons with on-board electronic libraries of images.

Crouse, K. R.↗

NASA NTRS1980

The recognition of extraterrestrial artificial signals

Considerations in the design of receivers for the detection and recognition of artificial microwave signals of extraterrestrial origin are discussed. Following a review of the objectives of SETI and the probable reception and detection characteristics of extraterrestrial signals, means for the improvement of the sensitivity, signal-to-noise ratios and on-line data processing capabilities of SETI receivers are indicated. The characteristics of the signals likely to be present at the output of an ultra-low-noise microwave receiver are then examined, including the system background noise, terrestrial radiations, astrophysical radiations, accidental artificial radiations of terrestrial origin, and intentional radiations produced by humans and by extraterrestrial intelligence. The classes of extraterrestrial signals likely to be detected, beacons and leakage signals, are considered, and options in the specification of gating and thresholding for a high-spectral resolution, high-time-resolution signal discriminator are indicated. Possible tests for the nonhuman origin of a received signal are also pointed out.

Seeger, C. L.↗

NASA NTRS1992

A catalog of low surface brightness galaxies - List II

A list of galaxies characterized by low surface brightness (LSB) is presented which facilitates the recognition of galaxies with brightnesses close to that of the sky. A total of 198 objects and 140 objects are listed in the primary and secondary catalogs respectively, and LSB galaxies are examined by means of H I redshift distributions. LSB disk galaxies are shown to have similar sizes and masses as the high-surface-brightness counterparts, and ellipticals and SOs are rarely encountered. Many LSB spirals have stellarlike nuclei, and most of the galaxies in the present catalog are late-type galaxies in the Sc, Sm, and Im classes. The LSB region of observational parameter space is shown to encompass a spectrum of types as full as that of the Hubble sequence. It is suggested that studies of LSB galaxies can provide important data regarding the formation and star-formation history of all galaxies.

Schombert, James M.↗

NASA NTRS1992

A neural network prototyping package within IRAF

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Bazell, D.↗

NASA NTRS2001

Runway Texture and Grid Pattern Effects on Rate-of-Descent Perception

To date, perceptual errors occur in determining descent rate from a computer-generated image in flight simulation. Pilots tend to touch down twice as hard in simulation than in flight, and more training time is needed in simulation before reaching steady-state performance. Barnes suggested that recognition of range may be the culprit, and he cited that problems such as collimated objects, binocular vision, and poor resolution lead to poor estimation of the velocity vector. Brown's study essentially ruled out that the lack of binocular vision is the problem. Dorfel added specificity to the problem by showing that pilots underestimated range in simulated scenes by 50% when 800 ft from the runway threshold. Palmer and Petitt showed that pilots are able to distinguish between a 1.7 ft/sec and 2.9 ft/sec sink rate when passively observing sink rates in a night scene. Platform motion also plays a role, as previous research has shown that the addition of substantial platform motion improves pilot estimates of vertical velocity and results in simulated touchdown rates more closely resembling flight. This experiment examined how some specific variations in the visual scene properties affect a pilot's perception of sink rate. It extended another experiment that focused on the visual and motion cues necessary for helicopter autorotations. In that experiment, pilots performed steep approaches to a runway. The visual content of the runway and its surroundings varied in two ways: texture and rectangular grid spacing. Four textures, included a no-texture case, were evaluated. Three grid spacings, including a no-grid case, were evaluated. The results showed that pilot better controlled their vertical descent rates when good texture cues were present. No significant differences were found for the grid manipulation. Using those visual scenes a simple psychophysics, experiment was performed. The purpose was to determine if the variations in the visual scenes allowed pilots to better perceive vertical velocity. To determine that answer, pilots passively viewed a particular visual scene in which the vehicle was descending at two different rates. Pilots had to select which of the two rates they thought was the fastest rate. The difference between the two rates changed using a staircase method, depending on whether or not the pilot was correct, until a minimum threshold between the two descent rates was reached. This process was repeated for all of the visual scenes to decide whether or not the visual scenes did allow pilots to perceive vertical velocity better among them. All of the data have yet to be analyzed; however, neither the effects of grid nor texture revealed any statistically significant trends. On further examination of the staircase method employed, a possibility exists that the lack of an evident trend may be due to the exit criterion used during the study. As such, the experiment will be repeated with an improved exit criterion in February. Results of this study will be presented in the submitted paper.

Schroeder, J. A.↗

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 NTRS1988

Sensor fusion of range and reflectance data for outdoor scene analysis

In recognizing objects in an outdoor scene, range and reflectance (or color) data provide complementary information. Results of experiments in recognizing outdoor scenes containing roads, trees, and cars are presented. The recognition program uses range and reflectance data obtained by a scanning laser range finder, as well as color data from a color TV camera. After segmentation of each image into primitive regions, models of objects are matched using various properties.

Kweon, In SO↗