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An extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using Moment Invariants as features of object representation is discussed. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.
Because of the continually changing environment of a space station, visual feedback is a vital element of a telerobotic system. A real time visual servoing system would allow a telerobot to track and manipulate randomly moving objects. Methodologies for the automatic selection of image features to be used to visually control the relative position between an eye-in-hand telerobot and a known object are devised. A weighted criteria function with both image recognition and control components is used to select the combination of image features which provides the best control. Simulation and experimental results of a PUMA robot arm visually tracking a randomly moving carburetor gasket with a visual update time of 70 milliseconds are discussed.
This paper reports an extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using movement invariants as features of object representation. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.
Pattern-recognition technologies developed by NASA to identify spacecraft and other objects in space have helped in the development of new, biometrics-based security solutions on Earth that recognize individuals to grant access to protected facilities, equipment, or information.
The multilateral Earth Observations Working Group (EOWG) was chartered in May 2012 in order to improve coordination and collaboration of Earth observing payloads, research, and applications on the International Space Station (ISS). The EOWG derives its authority from the ISS Program Science Forum, and a NASA representative serves as a permanent co-chair. A rotating co-chair position can be occupied by any of the international partners, following concurrence by the other partners; a JAXA representative is the current co-chair. Primary functions of the EOWG include, 1) the exchange of information on plans for payloads, from science and application objectives to instrument development, data collection, distribution and research; 2) recognition and facilitation of opportunities for international collaboration in order to optimize benefits from different instruments; and 3) provide a formal ISS Program interface for collection and application of remotely sensed data collected in response to natural disasters through the International Charter, Space and Major Disasters. Recent examples of EOWG activities include coordination of bilateral data sharing protocols between NASA and TsNIIMash for use of crew time and instruments in support of ATV5 reentry imaging activities; discussion of continued use and support of the Nightpod camera mount system by NASA and ESA; and review and revision of international partner contributions on Earth observations to the ISS Program Benefits to Humanity publication.
An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.
The outer Solar System has long appeared to be a largely empty place, inhabited only by the four giant planets, Pluto and a transient population of comets. In 1977 however, a faint and enigmatic object - 2060 Chiron - was discovered moving on a moderately inclined, strongly chaotic 51-year orbit which takes it from just inside Saturn's orbit out almost as far as that of Uranus. It was not initially clear from where Chiron originated. these objects become temporarily trapped on Centaur-like orbits Following Chiron's discovery, almost 15 years elapsed before other similar objects were discovered; five more have now been identified. Based on the detection statistics implied by these discoveries, it has become clear that these objects belong to a significant population of several hundred (or possibly several thousand) large icy bodies moving on relatively short-lived orbits between the giant planets. This new class of objects, known collectively as the Centaurs, are intermediate in diameter between typical comets (1-20 km) and small icy planets such as Pluto (approx. 2,300 km) and Triton (approx. 2,700 km). Although the Centaurs are interesting in their own right, they have taken on added significance following the recognition that they most probably originated in the ancient reservoir of comets and larger objects located beyond the orbit of Neptune known as the Kuiper belt.
The study for which the results are presented had these objectives: (1) to use Landsat data and computer-implemented pattern recognition to classify the major crops from regions encompassing different climates, soils, and crops; (2) to estimate crop areas for counties and states by using crop identification data obtained from the Landsat identifications; and (3) to evaluate the accuracy, precision, and timeliness of crop area estimates obtained from Landsat data. The paper describes the method of developing the training statistics and evaluating the classification accuracy. Landsat MSS data were adequate to accurately identify wheat in Kansas; corn and soybean estimates for Indiana were less accurate. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels.
Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.
The objective of this study was to evaluate two techniques, Flight Condition Recognition (FCR) and Flight Load Synthesis (FLS), for usage monitoring and assess the potential benefits of extending the retirement intervals of life-limited components, thus reducing the operator's maintenance and replacement costs. Both techniques involve indirect determination of loads using measured flight parameters and subsequent fatigue analysis to calculate the life expended on the life-limited components. To assess the potential benefit of usage monitoring, the two usage techniques were compared to current methods of component retirement. In addition, comparisons were made with direct load measurements to assess the accuracy of the two techniques. The data that was used for the evaluation of the usage monitoring techniques was collected under an independent HUMS Flight trial program, using a commercially available HUMS and data recording system. The usage data collect from the HUMS trial aircraft was analyzed off-line using PC-based software that included the FCR and FLS techniques. In the future, if the technique prove feasible, usage monitoring would be incorporated into the onboard HUMS.
The objective of this study was to evaluate two techniques, Flight Condition Recognition (FCR) and Flight Load Synthesis (FIS), for usage monitoring and assess the potential benefits of extending the retirement intervals of life-limited components, thus reducing the operator's maintenance and replacement costs. Both techniques involve indirect determination of loads using measured flight parameters and subsequent fatigue analysis to calculate the life expended on the life-limited components. To assess the potential benefit of usage monitoring, the two usage techniques were compared to current methods of component retirement. In addition, comparisons were made with direct load measurements to assess the accuracy of the two techniques.
Technique developed to reduce data quickly and objectively in images of phase-change paints. Involves use of three-tube color-camera system. Camera modified so separate red (R), green (G), and blue (B) video outputs contain complete framing information recorded and digitized by use of standard video format. Proven very simple and repeatable. Used wherever rapid and accurate remote temperature-profile measurements desired; for example, for test objects in inaccessible, toxic, or other hostile areas.
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.
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.
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.
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.
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.
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.