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At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRS1995

Laser Opto-Electronic Correlator for Robotic Vision Automated Pattern Recognition

A compact laser opto-electronic correlator for pattern recognition has been designed, fabricated, and tested. Specifically it is a translation sensitivity adjustable compact optical correlator (TSACOC) utilizing convergent laser beams for the holographic filter. Its properties and performance, including the location of the correlation peak and the effects of lateral and longitudinal displacements for both filters and input images, are systematically analyzed based on the nonparaxial approximation for the reference beam. The theoretical analyses have been verified in experiments. In applying the TSACOC to important practical problems including fingerprint identification, we have found that the tolerance of the system to the input lateral displacement can be conveniently increased by changing a geometric factor of the system. The system can be compactly packaged using the miniature laser diode sources and can be used in space by the National Aeronautics and Space Administration (NASA) and ground commercial applications which include robotic vision, and industrial inspection of automated quality control operations. The personnel of Standard International will work closely with the Jet Propulsion Laboratory (JPL) to transfer the technology to the commercial market. Prototype systems will be fabricated to test the market and perfect the product. Large production will follow after successful results are achieved.

Laser Opto-Electronic↗

NASA NTRS2007

Robust Fuzzy Controllers Using FPGAs

Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

Monroe, Author Gene S., Jr.↗

NASA NTRS1999

Emergence of postural patterns as a function of vision and translation frequency

Emergence of postural patterns as a function of vision and translation frequency. We examined the frequency characteristics of human postural coordination and the role of visual information in this coordination. Eight healthy adults maintained balance in stance during sinusoidal support surface translations (12 cm peak to peak) in the anterior-posterior direction at six different frequencies. Changes in kinematic and dynamic measures revealed that both sensory and biomechanical constraints limit postural coordination patterns as a function of translation frequency. At slow frequencies (0.1 and 0.25 Hz), subjects ride the platform (with the eyes open or closed). For fast frequencies (1.0 and 1.25 Hz) with the eyes open, subjects fix their head and upper trunk in space. With the eyes closed, large-amplitude, slow-sway motion of the head and trunk occurred for fast frequencies above 0.5 Hz. Visual information stabilized posture by reducing the variability of the head's position in space and the position of the center of mass (CoM) within the support surface defined by the feet for all but the slowest translation frequencies. When subjects rode the platform, there was little oscillatory joint motion, with muscle activity limited mostly to the ankles. To support the head fixed in space and slow-sway postural patterns, subjects produced stable interjoint hip and ankle joint coordination patterns. This increase in joint motion of the lower body dissipated the energy input by fast translation frequencies and facilitated the control of upper body motion. CoM amplitude decreased with increasing translation frequency, whereas the center of pressure amplitude increased with increasing translation frequency. Our results suggest that visual information was important to maintaining a fixed position of the head and trunk in space, whereas proprioceptive information was sufficient to produce stable coordinative patterns between the support surface and legs. The CNS organizes postural patterns in this balance task as a function of available sensory information, biomechanical constraints, and translation frequency.

NASA Discipline Neuroscience↗

NASA NTRS2010

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

Rahman, Zia-ur↗

NASA NTRS1996

Patterned Video Sensors For Low Vision

Miniature video cameras containing photoreceptors arranged in prescribed non-Cartesian patterns to compensate partly for some visual defects proposed. Cameras, accompanied by (and possibly integrated with) miniature head-mounted video display units restore some visual function in humans whose visual fields reduced by defects like retinitis pigmentosa.

Juday, Richard D.↗

NASA NTRS2012

Automatic Estimation of Volcanic Ash Plume Height using WorldView-2 Imagery

We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital elevation map, classification, the height of the ash plume can also be inferred. We present the results from applying this approach to six scenes acquired on two separate days in April and May of 2010 of the Eyjafjallajokull eruption in Iceland. These results show rough agreement with ash plume height estimates from visual and radar based measurements.

pattern recognition↗

NASA NTRS2016

Up Periscope! Designing a New Perceptual Metric for Imaging System Performance

Modern electronic imaging systems include optics, sensors, sampling, noise, processing, compression, transmission and display elements, and are viewed by the human eye. Many of these elements cannot be assessed by traditional imaging system metrics such as the MTF. More complex metrics such as NVTherm do address these elements, but do so largely through parametric adjustment of an MTF-like metric. The parameters are adjusted through subjective testing of human observers identifying specific targets in a set of standard images. We have designed a new metric that is based on a model of human visual pattern classification. In contrast to previous metrics, ours simulates the human observer identifying the standard targets. One application of this metric is to quantify performance of modern electronic periscope systems on submarines.

imaging↗

NASA NTRS1985

Electroacoustical imaging technique for encoding incoherent radiance fields as Gabor elementary signals

A technique is presented for directly encoding incoherent radiance fields as Gabor elementary signals. This technique uses an electro-acoustic sensor to modulate the electronic charges induced by the incident radiance field with the electric fields generated by Gaussian modulated sinusoidal acoustic waves. The resultant signal carries the amplitude and phase information required for localizing spatial frequencies of the radiance field. These localized spatial frequency representations provide a link between the either geometric or Fourier transform representations currently used in computer vision and pattern recognition.

Fales, C. L.↗

NASA NTRS1992

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Keller, James M.↗

NASA NTRS1993

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Keller, James M.↗

NASA NTRS2005

Vision and "Hand-Eye" Coordination for the Fujitsu HOAP-2 Humanoid Robot

Software was developed for a Fujitsu HOAP-2 humanoid robot to demonstrate that such "human form" robots can be used to perform useful tasks in an unknown environment; specifically, that they can operate with or in the place of humans to carry out construction operations necessary for space exploration, such as building or maintaining planetary surface habitats for humans. This paper describes the autonomous vision and hand-eye coordination software that enables the HOAP-2 to locate and manipulate objects. Simple vision and pattern detection algorithms including color filtering, noise filtering, image segmentation, orientation filters, and a feedback controlled tracking system enable the robot to locate marked building materials in the workspace. Experimentally determined spatial mappings associate locations in the visual field with the arm joint angles and trajectories necessary to reach these locations, allowing the robot to reach for and manipulate the building materials. These capabilities, when combined with locomotion capabilities, enable the robot to operate autonomously in an unknown workspace.

Moore, Jeffrey D.↗

NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗

NASA NTRS1990

Fast Feature-Recognizing Optoelectronic System

Proposed optoelectronic system recognizes features or classifies images by processing outputs of photosensors rapidly, in parallel, through circuits developed in research on neural networks. Array of photoconductive elements serve as photomodulated connections in electronic neural network, which provides high speed data compression to generate feature vector. System able to "learn" new patterns for subsequent recognition. Potential applications in robotic vision systems and pattern recognition.

Thakoor, S.↗

NASA NTRS1983

What does the eye see best?

The quantum efficiency of the human eye is measured for detecting a wide variety of spatiotemporal patterns using foveal vision in bright light. The performance of an ideal visual detector whose weighting function is matched to the signal is analyzed in the abstract, and the conditions of a two-interval forced-choice experiment are described. The best stimulus found is a small, briefly exposed circular patch of sinusoidal grating having a spatial frequency of about 7 c/deg, drifting at about 4 Hz. It is proposed that this is the weighting function of the most efficient human contrast detector. This function resembles the receptive field sensitivity profiles of many cortical neurons. The detectors resemble those derived from other recent experiments on both detection and discrimination and may account for a broad range of previous psychophysical results.

Watson, A. B.↗

NASA NTRS1990

Data compression in digitized lines

The problem of data compression is very important in digital photogrammetry, computer assisted cartography, and GIS/LIS. In addition, it is also applicable in many other fields such as computer vision, image processing, pattern recognition, and artificial intelligence. Consequently, there are many algorithms available to solve this problem but none of them are considered to be satisfactory. In this paper, a new method of finding critical points in a digitized curve is explained. This technique, based on the normalized symmetric scattered matrix, is good for both critical points detection and data compression. In addition, the critical points detected by this algorithm are compared with those by zero-crossings.

Thapa, Khagendra↗

NASA NTRS1989

Mars Rover imaging systems and directional filtering

Computer literature searches were carried out at Duke University and NASA Langley Research Center. The purpose is to enhance personal knowledge based on the technical problems of pattern recognition and image understanding which must be solved for the Mars Rover and Sample Return Mission. Intensive study effort of a large collection of relevant literature resulted in a compilation of all important documents in one place. Furthermore, the documents are being classified into: Mars Rover; computer vision (theory); imaging systems; pattern recognition methodologies; and other smart techniques (AI, neural networks, fuzzy logic, etc).

Wang, Paul P.↗

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 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.↗