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Sample records for digital pattern recognition

  1. Rapid detection of malignant bio-species using digital holographic pattern recognition and nano-photonics

    NASA Astrophysics Data System (ADS)

    Sarkisov, Sergey S.; Kukhtareva, Tatiana; Kukhtarev, Nickolai V.; Curley, Michael J.; Edwards, Vernessa; Creer, Marylyn

    2013-03-01

    There is a great need for rapid detection of bio-hazardous species particularly in applications to food safety and biodefense. It has been recently demonstrated that the colonies of various bio-species could be rapidly detected using culture-specific and reproducible patterns generated by scattered non-coherent light. However, the method heavily relies on a digital pattern recognition algorithm, which is rather complex, requires substantial computational power and is prone to ambiguities due to shift, scale, or orientation mismatch between the analyzed pattern and the reference from the library. The improvement could be made, if, in addition to the intensity of the scattered optical wave, its phase would be also simultaneously recorded and used for the digital holographic pattern recognition. In this feasibility study the research team recorded digital Gabor-type (in-line) holograms of colonies of micro-organisms, such as Salmonella with a laser diode as a low-coherence light source and a lensless high-resolution (2.0x2.0 micron pixel pitch) digital image sensor. The colonies were grown in conventional Petri dishes using standard methods. The digitally recorded holograms were used for computational reconstruction of the amplitude and phase information of the optical wave diffracted on the colonies. Besides, the pattern recognition of the colony fragments using the cross-correlation between the digital hologram was also implemented. The colonies of mold fungi Altenaria sp, Rhizophus, sp, and Aspergillus sp have been also generating nano-colloidal silver during their growth in specially prepared matrices. The silver-specific plasmonic optical extinction peak at 410-nm was also used for rapid detection and growth monitoring of the fungi colonies.

  2. Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

    NASA Astrophysics Data System (ADS)

    Lhamon, Michael Earl

    A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

  3. Illumination analysis of the digital pattern recognition system by Bessel masks and one-dimensional signatures

    NASA Astrophysics Data System (ADS)

    Solorza, S.; Álvarez-Borrego, J.

    2013-11-01

    The effects of illumination variations in digital images are a trend topic of the pattern recognition field. The luminance information of the objects help to classify them, however the environment illumination could cause a lot of problem if the system is not illumination invariant. Some applications of this topic include image and video quality, biometrics classification, etc. In this work an illumination analysis for a digital system invariant to position and rotation based on Fourier transform, Bessel masks, one-dimensional signatures and linear correlations are presented. The digital system was tested using a reference database of 21 fossil diatoms images of gray-scale and 307 x 307 pixels. The digital system has shown an excellent performance in the classification of 60,480 problem images which have different non-homogeneous illumination.

  4. Using commercial photo camera's RAW-based images in optical-digital correlator for pattern recognition

    NASA Astrophysics Data System (ADS)

    Starikov, Sergey N.; Konnik, Mikhail V.

    2008-03-01

    In optical-digital correlators for pattern recognition, linear registration of correlation signals is significant for both of recognition reliability and possible input image restoration. This usually achieves with scientific graduated technical cameras, but most of commercial digital cameras now have an option of RAW data output. With appropriate software and parameters of processing, it is possible to get linearized image data from photo camera's RAW file. Application of such photo cameras makes optical-digital systems cheaper, more flexible and brings along their wider propagation. For linear registration of correlation signals, open-source Dave Coffins's RAW converter DCRAW was used in this work. Data from photo camera were linearized by DCRAW converter in "totally RAW documental mode" with 16-bit output. Experimental results of comparison between linearized and non-linearized correlation signals and digitally restored input scene images are presented. It is shown, that applied linearization allows to increase linear dynamic range for used Canon EOS 400D camera more that 3 times.

  5. Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

    NASA Technical Reports Server (NTRS)

    Juday, Richard D. (Editor)

    1988-01-01

    The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

  6. Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

    SciTech Connect

    Juday, R.D.

    1988-01-01

    The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

  7. A digital procedure for ground water recharge and discharge pattern recognition and rate estimation.

    PubMed

    Lin, Yu-Feng; Anderson, Mary P

    2003-01-01

    A digital procedure to estimate recharge/discharge rates that requires relatively short preparation time and uses readily available data was applied to a setting in central Wisconsin. The method requires only measurements of the water table, fluxes such as stream baseflows, bottom of the system, and hydraulic conductivity to delineate approximate recharge/discharge zones and to estimate rates. The method uses interpolation of the water table surface, recharge/discharge mapping, pattern recognition, and a parameter estimation model. The surface interpolator used is based on the theory of radial basis functions with thin-plate splines. The recharge/discharge mapping is based on a mass-balance calculation performed using MODFLOW. The results of the recharge/discharge mapping are critically dependent on the accuracy of the water table interpolation and the accuracy and number of water table measurements. The recharge pattern recognition is performed with the help of a graphical user interface (GUI) program based on several algorithms used in image processing. Pattern recognition is needed to identify the recharge/discharge zonations and zone the results of the mapping method. The parameter estimation program UCODE calculates the parameter values that provide a best fit between simulated heads and flows and calibration head-and-flow targets. A model of the Buena Vista Ground Water Basin in the Central Sand Plains of Wisconsin is used to demonstrate the procedure.

  8. Digital image pattern recognition system using normalized Fourier transform and normalized analytical Fourier-Mellin transform

    NASA Astrophysics Data System (ADS)

    Vélez-Rábago, Rodrigo; Solorza-Calderón, Selene; Jordan-Aramburo, Adina

    2016-12-01

    This work presents an image pattern recognition system invariant to translation, scale and rotation. The system uses the Fourier transform to achieve the invariance to translation and the analytical Forier-Mellin transform for the invariance to scale and rotation. According with the statistical theory of box-plots, the pattern recognition system has a confidence level at least of 95.4%.

  9. Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

    1987-01-01

    The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

  10. Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

    1987-01-01

    The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

  11. Optical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.; Jutamulia, Suganda

    2008-10-01

    Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

  12. Pattern recognition technique

    NASA Technical Reports Server (NTRS)

    Hong, J. P.

    1971-01-01

    Technique operates regardless of pattern rotation, translation or magnification and successfully detects out-of-register patterns. It improves accuracy and reduces cost of various optical character recognition devices and page readers and provides data input to computer.

  13. Computer-implemented land use classification with pattern recognition software and ERTS digital data. [Mississippi coastal plains

    NASA Technical Reports Server (NTRS)

    Joyce, A. T.

    1974-01-01

    Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

  14. Automated Categorization Scheme for Digital Libraries in Distance Learning: A Pattern Recognition Approach

    ERIC Educational Resources Information Center

    Gunal, Serkan

    2008-01-01

    Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is…

  15. Pattern recognition in bioinformatics.

    PubMed

    de Ridder, Dick; de Ridder, Jeroen; Reinders, Marcel J T

    2013-09-01

    Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained.

  16. Optical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.; Jutamulia, Suganda

    1998-06-01

    This book provides a comprehensive review of optical pattern recognition, covering theoretical aspects as well as details of practical implementations and signal processing techniques. The first chapter is devoted to pattern recognition performed with optical correlators. Later chapters discuss new approaches based on neural networks, wavelet transforms, and the fractional Fourier transform. The book also covers nonlinear filter methods and optical-electronic hybrid systems. The final part deals with the devices and materials employed in modern systems, such as photorefractive crystals, microlasers, and liquid crystal spatial light modulators. The volume gives many examples of working systems that integrate optics, electronics, and computers, and it covers a range of new developments from mathematical theories to novel optical materials. It will be of great interest to graduate students and researchers in optical engineering and machine vision.

  17. Advanced Pattern Recognition.

    DTIC Science & Technology

    1983-05-01

    classification via statistical pattern recognition; image preprocessing, enhancement, and filtering; image warping , resampling, and point positioning; and...obj_region training files *•* Edit Programs »*» mode_filter ( mdf ) - mode filtering of a classified image (noise cleaning) edge_thin - thin... mdf 5 5 comments: experimental Method to segregate Water, Urban, Vegetation urban edges method_type: edge measurements: avg 3/ep_smooth 2

  18. Pattern Recognition Control Design

    NASA Technical Reports Server (NTRS)

    Gambone, Elisabeth A.

    2018-01-01

    Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

  19. Pattern Recognition Control Design

    NASA Technical Reports Server (NTRS)

    Gambone, Elisabeth

    2016-01-01

    Spacecraft control algorithms must know the expected spacecraft response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach can be used to investigate the relationship between the control effector commands and the spacecraft responses. Instead of supplying the approximated vehicle properties and the effector performance characteristics, a database of information relating the effector commands and the desired vehicle response can be used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands can be analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center (Ref. 1) to analyze flight dynamics Monte Carlo data sets through pattern recognition methods can be used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands is established, it can be used in place of traditional control laws and gains set. This pattern recognition approach can be compared with traditional control algorithms to determine the potential benefits and uses.

  20. Fuzzy Logic-Based Audio Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Malcangi, M.

    2008-11-01

    Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

  1. Image Recognition Based on Biometric Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Sun, Shuliang; Chen, Zhong; Liu, Chenglian; Guo, Yongning; Lin, Xueyun

    2011-09-01

    A new method, biomimetric pattern recognition, is mentioned to recognize images. At first, the image is pretreatment and feature extraction, then a high vector is got. A biomimetric pattern recognition model is designed. The judgment function is used to discriminate the classification of the samples. It is showed that the method is effective for little samples by experiment. It would be useful in many fields in future.

  2. Smart pattern recognition

    NASA Astrophysics Data System (ADS)

    Alfalou, A.; Brosseau, C.; Alam, M. S.

    2013-03-01

    The purpose of this paper is to test correlation methods for pattern recognition applications. A broad overview of the main correlation architectures is first given. Many correlation data are compared with those obtained from standard pattern recognition methods. We used our simulations to predict improved decisional performance from correlation methods. More specifically, we are focused on the POF filter and composite filter family. We present an optimized composite correlation filter, called asymmetric segmented phase-only filter (ASPOF) for mobile target recognition applications. The main objective is to find a compromise between the number of references to be merged in the correlation filter and the time needed for making a decision. We suggest an all-numerical implementation of a VanderLugt (VLC) type composite filter. The aim of this all-numerical implementation is to take advantage of the benefits of the correlation methods and make the correlator easily reconfigurable for various scenarios. The use of numerical implementation of the optical Fourier transform improves the decisional performance of the correlator. Further, it renders the correlator less sensitive to the saturation phenomenon caused by the increased number of references used for fabricating the composite filter. Different tests are presented making use of the peak-to-correlation energy criterion and ROC curves. These tests confirm the validity ofour technique. Elderly fall detection and underwater mine detection are two applications which are considered for illustrating the benefits of our approach. The present work is motivated by the need for detailed discussions of the choice of the correlation architecture for these specific applications, pre-processing in the input plane and post processing in the output plane techniques for such analysis.

  3. Pattern recognition systems and procedures

    NASA Technical Reports Server (NTRS)

    Nelson, G. D.; Serreyn, D. V.

    1972-01-01

    The objectives of the pattern recognition tasks are to develop (1) a man-machine interactive data processing system; and (2) procedures to determine effective features as a function of time for crops and soils. The signal analysis and dissemination equipment, SADE, is being developed as a man-machine interactive data processing system. SADE will provide imagery and multi-channel analog tape inputs for digitation and a color display of the data. SADE is an essential tool to aid in the investigation to determine useful features as a function of time for crops and soils. Four related studies are: (1) reliability of the multivariate Gaussian assumption; (2) usefulness of transforming features with regard to the classifier probability of error; (3) advantage of selecting quantizer parameters to minimize the classifier probability of error; and (4) advantage of using contextual data. The study of transformation of variables (features), especially those experimental studies which can be completed with the SADE system, will be done.

  4. Pattern recognition in spectra

    NASA Astrophysics Data System (ADS)

    Gebran, M.; Paletou, F.

    2017-06-01

    We present a new automated procedure that simultaneously derives the effective temperature Teff, surface gravity log g, metallicity [Fe/H], and equatorial projected rotational velocity ve sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones.

  5. Applications of chaotic neurodynamics in pattern recognition

    NASA Astrophysics Data System (ADS)

    Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

    1991-08-01

    Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is

  6. Isolated digit recognition without time alignment

    NASA Astrophysics Data System (ADS)

    Gay, Jeffrey Mark

    1994-12-01

    This thesis examines methods for isolated digit recognition without using time alignment. Resource requirements for isolated word recognizers that use time alignment can become prohibitively large as the vocabulary to be classified grows. Thus, methods capable of achieving recognition rates comparable to those obtained with current methods using these techniques are needed. The goals of this research are to find feature sets for speech recognition that perform well without using time alignment, and to identify classifiers that provide good performance with these features. Using the digits from the TI46 database, baseline speaker-independent recognition rates of 95.2% for the complete speaker set and 98.1% for the male speaker set are established using dynamic time warping (DTW). This work begins with features derived from spectrograms of each digit. Based on a critical band frequency scale covering the telephone bandwidth (300-3000 Hz), these critical band energy features are classified alone and in combination with several other feature sets, with several different classifiers. With this method, there is one 'short' feature vector per word. For speaker-independent recognition using the complete speaker set and a multi-layer perceptron (MLP) classifier, a recognition rate of 92.4% is achieved. For the same classifier with the male speaker set, a recognition rate of 97.1% is achieved. For the male speaker set, there is no statistical difference between results using DTW, and those using the MLP and no time alignment. This shows that there are feature sets that may provide high recognition rates for isolated word recognition without the need for time alignment.

  7. Speech Recognition for A Digital Video Library.

    ERIC Educational Resources Information Center

    Witbrock, Michael J.; Hauptmann, Alexander G.

    1998-01-01

    Production of the meta-data supporting the Informedia Digital Video Library interface is automated using techniques derived from artificial intelligence research. Speech recognition and natural-language processing, information retrieval, and image analysis are applied to produce an interface that helps users locate information and navigate more…

  8. Speech Recognition for A Digital Video Library.

    ERIC Educational Resources Information Center

    Witbrock, Michael J.; Hauptmann, Alexander G.

    1998-01-01

    Production of the meta-data supporting the Informedia Digital Video Library interface is automated using techniques derived from artificial intelligence research. Speech recognition and natural-language processing, information retrieval, and image analysis are applied to produce an interface that helps users locate information and navigate more…

  9. Inverse scattering approach to improving pattern recognition

    NASA Astrophysics Data System (ADS)

    Chapline, George; Fu, Chi-Yung

    2005-05-01

    The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

  10. Inverse Scattering Approach to Improving Pattern Recognition

    SciTech Connect

    Chapline, G; Fu, C

    2005-02-15

    The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the ''wake-sleep'' algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

  11. Stimulus length uncertainty with dichotic digit recognition.

    PubMed

    Strouse, A; Wilson, R H

    1999-04-01

    Dichotic digit listening was made more difficult by interleaving one-, two-, three-, and four-pair digits within a test list in which the subjects did not know a priori the number of digit pairs presented on a given trial, thereby introducing listener uncertainty. Twenty right-handed (mean age = 26.8 years) and 20 left-handed adults (mean age = 24.0) with normal hearing and 40 right-handed adults in the 60- to 75-years age range (mean age = 67.2) with mild-to-moderate sensorineural hearing loss were evaluated. Compared to traditional paradigms, the uncertainty of stimulus length (one, two, three, or four pairs) reduced overall recognition performance on stimuli presented to both ears of all groups, but the reduction was more pronounced for the left ear than for the right ear. The recognition performance of the right-handed subjects was more homogeneous than the performance of the left-handed subjects. In comparison to the young subjects, the 60- to 75-year-old group had substantially reduced recognition performance.

  12. Pattern-Recognition Processor Using Holographic Photopolymer

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Cammack, Kevin

    2006-01-01

    proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square

  13. Optical pattern recognition for printed music notation

    NASA Astrophysics Data System (ADS)

    Homenda, Wladyslaw

    1995-03-01

    The paper presents problems related to automated recognition of printed music notation. Music notation recognition is a challenging problem in both fields: pattern recognition and knowledge representation. Music notation symbols, though well characterized by their features, are arranged in elaborated way in real music notation, which makes recognition task very difficult and still open for new ideas. On the other hand, the aim of the system, i.e. application of acquired printed music into further processing requires special representation of music data. Due to complexity of music nature and music notation, music representation is one of the key issue in music notation recognition and music processing. The problems of pattern recognition and knowledge representation in context or music processing are discussed in this paper. MIDISCAN, the computer system for music notation recognition and music processing, is presented.

  14. Supervised pattern recognition in food analysis.

    PubMed

    Berrueta, Luis A; Alonso-Salces, Rosa M; Héberger, Károly

    2007-07-27

    Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.

  15. Pattern activation/recognition theory of mind

    PubMed Central

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a “Pattern Recognition Theory of Mind” that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call “Pattern Activation/Recognition Theory of Mind.” While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation. PMID:26236228

  16. Pattern activation/recognition theory of mind.

    PubMed

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.

  17. Visual cluster analysis and pattern recognition methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    2001-01-01

    A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  18. Pattern recognition using linguistic fuzzy logic predictors

    NASA Astrophysics Data System (ADS)

    Habiballa, Hashim

    2016-06-01

    The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.

  19. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    NASA Astrophysics Data System (ADS)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  20. Pattern recognition system and procedures

    NASA Technical Reports Server (NTRS)

    Nelson, G. D.; Serreyn, D. V.

    1972-01-01

    The ratio transformation technique is used to determine effective features as function of time in remote multiple sensing of crops and soils. The selection of quantizer parameters for a two-class recognition problem under the criteria of minimizing the probability of errors is also discussed.

  1. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    NASA Astrophysics Data System (ADS)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  2. Image processing and pattern recognition in textiles

    NASA Astrophysics Data System (ADS)

    Kong, Lingxue; She, F. H.

    2001-09-01

    Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into/with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work for fiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yields satisfactory results by giving a high level of accuracy in classification.

  3. Word recognition using ideal word patterns

    NASA Astrophysics Data System (ADS)

    Zhao, Sheila X.; Srihari, Sargur N.

    1994-03-01

    The word shape analysis approach to text recognition is motivated by discoveries in psychological studies of the human reading process. It attempts to describe and compare the shape of the word as a whole object without trying to segment and recognize the individual characters, so it bypasses the errors committed in character segmentation and classification. However, the large number of classes and large variation and distortion expected in all patterns belonging to the same class make it difficult for conventional, accurate, pattern recognition approaches. A word shape analysis approach using ideal word patterns to overcome the difficulty and improve recognition performance is described in this paper. A special word pattern which characterizes a word class is extracted from different sample patterns of the word class and stored in memory. Recognition of a new word pattern is achieved by comparing it with the special pattern of each word class called ideal word pattern. The process of generating the ideal word pattern of each word class is proposed. The algorithm was tested on a set of machine printed gray scale word images which included a wide range of print types and qualities.

  4. Molecular Recognition in the Digital Radio Domain

    NASA Astrophysics Data System (ADS)

    William D. Hunt,; Peter J. Edmonson,; Desmond D. Stubbs,; Sang-Hun Lee,

    2010-07-01

    In this paper we discuss the theoretical and experimental constructs which together point the way towards the transduction of biomolecular recognition events into a palpable set of electrical signals. This combines the applied physics of surface perturbations on acoustic wave device surfaces and the biochemistry of the interactions between an immobilized biomolecule (e.g., an antibody) and a target molecule which is flowing past the sensor surface (e.g., an antigen). We will first provide the theoretical basis for our contention that we can extract information about both molecular recognition and conformational change from the electrical signal and will then confirm this assertion with experimental results relating to induced conformational changes in DNA on a quartz crystal microbalance (QCM) surface. Next we will discuss our digital radio technique whereby the real time measurements using antibody coated surface acoustic wave (SAW) devices in the vapor phase allow us to differentiate between close chemical analogs of nitro-based molecules (e.g., tri-nitro toluene vs musk oil) by virtue of the cross-reactivity of the antibody-antigen interaction. In immunochemistry this is referred to as antibody promiscuity. Finally, we present two- and three-dimensional plots illustrating our technique which derives much from in-phase and quadrature phase (IQ) mapping. The end result is a powerful technique which allows one to differentiate between target molecules and chemically similar interferrents.

  5. Pattern recognition using asymmetric attractor neural networks

    SciTech Connect

    Jin Tao; Zhao Hong

    2005-12-15

    The asymmetric attractor neural networks designed by the Monte Carlo- (MC-) adaptation rule are shown to be promising candidates for pattern recognition. In such a neural network with relatively low symmetry, when the members of a set of template patterns are stored as fixed-point attractors, their attraction basins are shown to be isolated islands embedded in a ''chaotic sea.'' The sizes of these islands can be controlled by a single parameter. We show that these properties can be used for effective pattern recognition and rejection. In our method, the pattern to be identified is attracted to a template pattern or a chaotic attractor. If the difference between the pattern to be identified and the template pattern is smaller than a predescribed threshold, the pattern is attracted to the template pattern automatically and thus is identified as belonging to this template pattern. Otherwise, it wanders in a chaotic attractor for ever and thus is rejected as an unknown pattern. The maximum sizes of these islands allowed by this kind of neural networks are determined by a modified MC-adaptation rule which are shown to be able to dramatically enlarge the sizes of the islands. We illustrate the use of our method for pattern recognition and rejection with an example of recognizing a set of Chinese characters.

  6. Pattern recognition and feature extraction with an optical Hough transform

    NASA Astrophysics Data System (ADS)

    Fernández, Ariel

    2016-09-01

    Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

  7. Optical Pattern Recognition for Missile Guidance.

    DTIC Science & Technology

    1979-10-01

    crystal, missile guidance, multi-sensor pattern recognition, normalized invariant moments, optical data processing , optical patterni recognition, photo...computing1 offers the attractive features vided in Sec. II for completeness and to enable future of parallel processing in real time and thus has been of...Fourier plane, Eq. (5) is used. rameter in coherent optical processing application. We In practice, MTF as defined is really a contrast transfer can

  8. Object recognition by use of polarimetric phase-shifting digital holography.

    PubMed

    Nomura, Takanori; Javidi, Bahram

    2007-08-01

    Pattern recognition by use of polarimetric phase-shifting digital holography is presented. Using holography, the amplitude distribution and phase difference distribution between two orthogonal polarizations of three-dimensional (3D) or two-dimensional phase objects are obtained. This information contains both complex amplitude and polarimetric characteristics of the object, and it can be used for improving the discrimination capability of object recognition. Experimental results are presented to demonstrate the idea. To the best of our knowledge, this is the first report on 3D polarimetric recognition of objects using digital holography.

  9. Optical recognition of statistical patterns

    NASA Technical Reports Server (NTRS)

    Lee, S. H.

    1981-01-01

    Optical implementation of the Fukunaga-Koontz transform (FKT) and the Least-Squares Linear Mapping Technique (LSLMT) is described. The FKT is a linear transformation which performs image feature extraction for a two-class image classification problem. The LSLMT performs a transform from large dimensional feature space to small dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. The FKT and the LSLMT were optically implemented by utilizing a coded phase optical processor. The transform was used for classifying birds and fish. After the F-K basis functions were calculated, those most useful for classification were incorporated into a computer generated hologram. The output of the optical processor, consisting of the squared magnitude of the F-K coefficients, was detected by a T.V. camera, digitized, and fed into a micro-computer for classification. A simple linear classifier based on only two F-K coefficients was able to separate the images into two classes, indicating that the F-K transform had chosen good features. Two advantages of optically implementing the FKT and LSLMT are parallel and real time processing.

  10. Optical recognition of statistical patterns

    NASA Astrophysics Data System (ADS)

    Lee, S. H.

    1981-12-01

    Optical implementation of the Fukunaga-Koontz transform (FKT) and the Least-Squares Linear Mapping Technique (LSLMT) is described. The FKT is a linear transformation which performs image feature extraction for a two-class image classification problem. The LSLMT performs a transform from large dimensional feature space to small dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. The FKT and the LSLMT were optically implemented by utilizing a coded phase optical processor. The transform was used for classifying birds and fish. After the F-K basis functions were calculated, those most useful for classification were incorporated into a computer generated hologram. The output of the optical processor, consisting of the squared magnitude of the F-K coefficients, was detected by a T.V. camera, digitized, and fed into a micro-computer for classification. A simple linear classifier based on only two F-K coefficients was able to separate the images into two classes, indicating that the F-K transform had chosen good features. Two advantages of optically implementing the FKT and LSLMT are parallel and real time processing.

  11. Associative Pattern Recognition In Analog VLSI Circuits

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1995-01-01

    Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

  12. Associative Pattern Recognition In Analog VLSI Circuits

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1995-01-01

    Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

  13. Multiple degree of freedom optical pattern recognition

    NASA Technical Reports Server (NTRS)

    Casasent, D.

    1987-01-01

    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.

  14. Conformal Predictions in Multimedia Pattern Recognition

    ERIC Educational Resources Information Center

    Nallure Balasubramanian, Vineeth

    2010-01-01

    The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

  15. Pattern Recognition by Retina-Like Devices.

    ERIC Educational Resources Information Center

    Weiman, Carl F. R.; Rothstein, Jerome

    This study has investigated some pattern recognition capabilities of devices consisting of arrays of cooperating elements acting in parallel. The problem of recognizing straight lines in general position on the quadratic lattice has been completely solved by applying parallel acting algorithms to a special code for lines on the lattice. The…

  16. Pattern Recognition For Automatic Visual Inspection

    NASA Astrophysics Data System (ADS)

    Fu, K. S.

    1982-11-01

    Three major approaches to pattern recognition, (1) template matching, (2) decision-theoretic approach, and (3) structural and syntactic approach, are briefly introduced. The application of these approaches to automatic visual inspection of manufactured products are then reviewed. A more general method for automatic visual inspection of IC chips is then proposed. Several practical examples are included for illustration.

  17. Prototype neural network pattern recognition testbed

    NASA Astrophysics Data System (ADS)

    Worrell, Steven W.; Robertson, James A.; Varner, Thomas L.; Garvin, Charles G.

    1991-02-01

    Recent successes ofneural networks has led to an optimistic outlook for neural network applications to image processing(IP). This paperpresents a general architecture for performing comparative studies of neural processing and more conventional IF techniques as well as hybrid pattern recognition (PR) systems. Two hybrid PR systems have been simulated each of which incorporate both conventional and neural processing techniques.

  18. Summary of 1971 pattern recognition program development

    NASA Technical Reports Server (NTRS)

    Whitley, S. L.

    1972-01-01

    Eight areas related to pattern recognition analysis at the Earth Resources Laboratory are discussed: (1) background; (2) Earth Resources Laboratory goals; (3) software problems/limitations; (4) operational problems/limitations; (5) immediate future capabilities; (6) Earth Resources Laboratory data analysis system; (7) general program needs and recommendations; and (8) schedule and milestones.

  19. Conformal Predictions in Multimedia Pattern Recognition

    ERIC Educational Resources Information Center

    Nallure Balasubramanian, Vineeth

    2010-01-01

    The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

  20. Dynamic Approaches for Facial Recognition Using Digital Image Speckle Correlation

    NASA Astrophysics Data System (ADS)

    Rafailovich-Sokolov, Sara; Guan, E.; Afriat, Isablle; Rafailovich, Miriam; Sokolov, Jonathan; Clark, Richard

    2004-03-01

    Digital image analysis techniques have been extensively used in facial recognition. To date, most static facial characterization techniques, which are usually based on Fourier transform techniques, are sensitive to lighting, shadows, or modification of appearance by makeup, natural aging or surgery. In this study we have demonstrated that it is possible to uniquely identify faces by analyzing the natural motion of facial features with Digital Image Speckle Correlation (DISC). Human skin has a natural pattern produced by the texture of the skin pores, which is easily visible with conventional digital cameras of resolution greater than 4 mega pixels. Hence the application of the DISC method to the analysis of facial motion appears to be very straightforward. Here we demonstrate that the vector diagrams produced by this method for facial images are directly correlated to the underlying muscle structure which is unique for an individual and is not affected by lighting or make-up. Furthermore, we will show that this method can also be used for medical diagnosis in early detection of facial paralysis and other forms of skin disorders.

  1. Implementation of age and gender recognition system for intelligent digital signage

    NASA Astrophysics Data System (ADS)

    Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

    2015-12-01

    Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

  2. Hybrid pattern recognition system for the robotic vision

    NASA Astrophysics Data System (ADS)

    Li, Yulin; Zhao, Mingjun M.; Zhao, Li

    1991-03-01

    The applications and development of hybrid image processing have attracted significant attention in recent. This paper describes a multifunction optoelectronic hybrid processor that can inplemente several operations . This new system is appropriate for real-time automatic pattern recognition. An effective approach and architecture are provided for the robotic vision. In the system utilizing the joint transform techniques and the resulting Fourier transform of the object image and reference image was detected by the CCD camera and then sent it into digital image preprocessor . At the same time Fourier spectrum of the edge-enhanced images are obtained pass coherent optical image processing through a liquid crystal spatial light modulator as a real-time interface device ( a incoherent-to-coherent irrge flCC ) . Thus classification and correlation of the object pattern are carried out by using both of digital and analogy inge processing. The preliminary experimental results are given. 1 .

  3. Pattern recognition with fast feature extraction

    NASA Astrophysics Data System (ADS)

    Nakhodkin, Mykola G.; Musatenko, Yurij S.; Kurashov, Vitalij N.

    1998-04-01

    The comparison of recently suggested algorithms of fast approximate Karhunen-Loeve (KL) transform when applied to pattern recognition problem is presented. It is known that adequate signs selection is still the problem. In the paper we consider several algorithms that can perform fast and qualitative signs selection. Among them are wavelet based algorithm of approximate KL transform, 2D algorithm of KL transform, algorithm with projection into proper orthogonalized basis, and real time algorithm of KL transform. To compare quality of the algorithms they are applied to human faces recognition problem. The efficiency of the all abovementioned algorithms is demonstrated.

  4. Pattern Recognition in Time Series

    NASA Astrophysics Data System (ADS)

    Lin, Jessica; Williamson, Sheri; Borne, Kirk D.; DeBarr, David

    2012-03-01

    , planetary transits), quasi-periodic variations (e.g., star spots, neutron star oscillations, active galactic nuclei), outburst events (e.g., accretion binaries, cataclysmic variable stars, symbiotic stars), transient events (e.g., gamma-ray bursts (GRB), flare stars, novae, supernovae (SNe)), stochastic variations (e.g., quasars, cosmic rays, luminous blue variables (LBVs)), and random events with precisely predictable patterns (e.g., microlensing events). Several such astrophysical phenomena are wavelength-specific cases, or were discovered as a result of wavelength-specific flux variations, such as soft gamma ray repeaters, x-ray binaries, radio pulsars, and gravitational waves. Despite the wealth of discoveries in this space of time variability, there is still a vast unexplored region, especially at low flux levels and short time scales (see also the chapter by Bloom and Richards in this book). Figure 28.1 illustrates the gap in astronomical knowledge in this time-domain space. The LSST project aims to explore phenomena in the time gap. In addition to flux-based time series, astronomical data also include motion-based time series. These include the trajectories of planets, comets, and asteroids in the Solar System, the motions of stars around the massive black hole at the center of the Milky Way galaxy, and the motion of gas filaments in the interstellar medium (e.g., expanding supernova blast wave shells). In most cases, the motions measured in the time series correspond to the actual changing positions of the objects being studied. In other cases, the detected motions indirectly reflect other changes in the astronomical phenomenon, such as light echoes reflecting across vast gas and dust clouds, or propagating waves.

  5. Pattern recognition monitoring of PEM fuel cell

    DOEpatents

    Meltser, M.A.

    1999-08-31

    The CO-concentration in the H{sub 2} feed stream to a PEM fuel cell stack is monitored by measuring current and voltage behavior patterns from an auxiliary cell attached to the end of the stack. The auxiliary cell is connected to the same oxygen and hydrogen feed manifolds that supply the stack, and discharges through a constant load. Pattern recognition software compares the current and voltage patterns from the auxiliary cell to current and voltage signature determined from a reference cell similar to the auxiliary cell and operated under controlled conditions over a wide range of CO-concentrations in the H{sub 2} fuel stream. 4 figs.

  6. Pattern recognition monitoring of PEM fuel cell

    DOEpatents

    Meltser, Mark Alexander

    1999-01-01

    The CO-concentration in the H.sub.2 feed stream to a PEM fuel cell stack is monitored by measuring current and voltage behavior patterns from an auxiliary cell attached to the end of the stack. The auxiliary cell is connected to the same oxygen and hydrogen feed manifolds that supply the stack, and discharges through a constant load. Pattern recognition software compares the current and voltage patterns from the auxiliary cell to current and voltage signature determined from a reference cell similar to the auxiliary cell and operated under controlled conditions over a wide range of CO-concentrations in the H.sub.2 fuel stream.

  7. Pattern recognitions receptors in immunodeficiency disorders.

    PubMed

    Mortaz, Esameil; Adcock, Ian M; Tabarsi, Payam; Darazam, Ilad Alavi; Movassaghi, Masoud; Garssen, Johan; Jamaati, Hamidreza; Velayati, Aliakbar

    2017-01-14

    Pattern recognition receptors (PRRs) recognize common microbial or host-derived macromolecules and have important roles in early activation and response of the immune system. Initiation of the innate immune response starts with the recognition of microbial structures called pathogen associated molecular patterns (PAMPs). Recognition of PAMPs is performed by germline-encoded receptors expressed mainly on immune cells termed pattern recognition receptors (PRRs). Several classes of pattern recognition receptors (PRRs) are involved in the pathogenesis of diseases, including Toll-like receptors (TLRs), C-type lectin receptors (CLRs), and Nod-like receptors (NLRs). Patients with primary immune deficiencies (PIDs) affecting TLR signaling can elucidate the importance of these proteins in the human immune system. Defects in interleukin-1 receptor-associated kinase-4 and myeloid differentiation factor 88 (MyD88) lead to susceptibility to infections with bacteria, while mutations in nuclear factor-κB essential modulator (NEMO) and other downstream mediators generally induce broader susceptibility to bacteria, viruses, and fungi. In contrast, TLR3 signaling defects are associated with susceptibility to herpes simplex virus type 1 encephalitis. Other PIDs induce functional alterations of TLR signaling pathways, such as common variable immunodeficiency in which plasmacytoid dendritic cell defects enhance defective responses of B cells to shared TLR agonists. Altered TLR responses to TLR2 and 4 agonists are seen in chronic granulomatous disease (CGD) and X-linked agammaglobulinemia (XLA). Enhanced TLR responses, meanwhile, are seen for TLRs 5 and 9 in CGD, TLRs 4, 7/8, and 9 in XLA, TLRs 2 and 4 in hyper IgE syndrome (HIES), and for most TLRs in adenosine deaminase deficiency. In this review we provide the reader with an update on the role of TLRs and downstream signaling pathways in PID disorders.

  8. Making digit patterns in the vertebrate limb.

    PubMed

    Tickle, Cheryll

    2006-01-01

    The vertebrate limb has been a premier model for studying pattern formation - a striking digit pattern is formed in human hands, with a thumb forming at one edge and a little finger at the other. Classic embryological studies in different model organisms combined with new sophisticated techniques that integrate gene-expression patterns and cell behaviour have begun to shed light on the mechanisms that control digit patterning, and stimulate re-evaluation of the current models.

  9. Pattern recognition receptors in antifungal immunity.

    PubMed

    Plato, Anthony; Hardison, Sarah E; Brown, Gordon D

    2015-03-01

    Receptors of the innate immune system are the first line of defence against infection, being able to recognise and initiate an inflammatory response to invading microorganisms. The Toll-like (TLR), NOD-like (NLR), RIG-I-like (RLR) and C-type lectin-like receptors (CLR) are four receptor families that contribute to the recognition of a vast range of species, including fungi. Many of these pattern recognition receptors (PRRs) are able to initiate innate immunity and polarise adaptive responses upon the recognition of fungal cell wall components and other conserved molecular patterns, including fungal nucleic acids. These receptors induce effective mechanisms of fungal clearance in normal hosts, but medical interventions, immunosuppression or genetic predisposition can lead to susceptibility to fungal infections. In this review, we highlight the importance of PRRs in fungal infection, specifically CLRs, which are the major PRR involved. We will describe specific PRRs in detail, the importance of receptor collaboration in fungal recognition and clearance, and describe how genetic aberrations in PRRs can contribute to disease pathology.

  10. Implementation of pattern recognition algorithm based on RBF neural network

    NASA Astrophysics Data System (ADS)

    Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel

    2002-12-01

    In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

  11. VLSI Microsystem for Rapid Bioinformatic Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Lue, Jaw-Chyng

    2009-01-01

    A system comprising very-large-scale integrated (VLSI) circuits is being developed as a means of bioinformatics-oriented analysis and recognition of patterns of fluorescence generated in a microarray in an advanced, highly miniaturized, portable genetic-expression-assay instrument. Such an instrument implements an on-chip combination of polymerase chain reactions and electrochemical transduction for amplification and detection of deoxyribonucleic acid (DNA).

  12. Pattern recognition and massively distributed computing.

    PubMed

    Davies, E Keith; Glick, Meir; Harrison, Karl N; Richards, W Graham

    2002-12-01

    A feature of Peter Kollman's research was his exploitation of the latest computational techniques to devise novel applications of the free energy perturbation method. He would certainly have seized upon the opportunities offered by massively distributed computing. Here we describe the use of over a million personal computers to perform virtual screening of 3.5 billion druglike molecules against protein targets by pharmacophore pattern matching, together with other applications of pattern recognition such as docking ligands without any a priori knowledge about the binding site location.

  13. Optical-digital-neural network system for aided target recognition

    NASA Astrophysics Data System (ADS)

    Farr, Keith B.; Hartman, Richard L.

    1995-07-01

    Many military systems have a critical need for aided target recognition, or cuing. This includes several systems with wide field-of-view search missions such as the UAV, EFOG-M, and Comanche. This report discusses one new approach: a multiple region of interest processor based on diffraction pattern sampling and digital neural network processing. In this concept an optical system segments the image into multiple, rectangular regions of interest and in parallel converts each ROI, be it visible, IR, or radar, to a spatial frequency power spectrum and samples that spectrum for 64 features. A neural network learns to correlate those features with target classes or identifications. A digital system uses the network weights to recognize unknown targets. The research discussed in this report using a single ROI processor showed a very high level of performance. Out of 1024 trials with models of five targets of F- 14, F-18, HIND, SCUD, and M1 tanks, there were 1023 correct classifications and 1 incorrect classification. Out of 1514 trials with those images plus 490 real clutter scenes, there were 1514 correct decisions between target or no-target. Of the 1024 target detections, there were 1023 correct classifications. Out of 60 trials with low resolution IR images of real scenes, there were 60 correct decisions between target and no-target. Of the 40 target detections, there were 40 correct classifications.

  14. Statistical pattern recognition for rock joint images

    NASA Astrophysics Data System (ADS)

    Wang, Weixing; Bin, Cui

    2005-10-01

    As a cooperation project between Sweden and China, we sampled a number of rock specimens for analyze rock fracture network by optical image technique. The samples are resin injected, in which way; opened fractures can be seen clearly by means of UV (Ultraviolet) light illumination. In the study period, Recognition of rock fractures is crucial in many rock engineering applications. In order to successfully applying automatic image processing techniques for the problem of automatic (or semi-automatic) rock fracture detection and description, the key (and hardest task) is the automatic detection of fractures robustly in images. When statistical pattern recognition is used to segment a rock joint color image, features of different samples can be learned first, then, each pixel of the image is classified by these features. As the testing result showing, an attribute rock fracture image is segmented satisfactorily by using this way. The method can be widely used for other complicated images too. In this paper, Kernel Fisher discrimination (KFD) is employed to construct a statistical pattern recognition classifier. KFD can transform nonlinear discrimination in an attribute space with high dimension, into linear discrimination in a feature space with low dimension. While one needs not know the detailed mapping form from attribute space to feature space in the process of transformation. It is proved that this method performs well by segmenting complicated rock joint color images.

  15. Developing Signal-Pattern-Recognition Programs

    NASA Technical Reports Server (NTRS)

    Shelton, Robert O.; Hammen, David

    2006-01-01

    Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

  16. Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: a catalogue of ˜500 000 H I gas fraction estimates based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Teimoorinia, Hossen; Ellison, Sara L.; Patton, David R.

    2017-02-01

    The application of artificial neural networks (ANNs) for the estimation of H I gas mass fraction (M_{H I}/{{M}_{*}}) is investigated, based on a sample of 13 674 galaxies in the Sloan Digital Sky Survey (SDSS) with H I detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters (g - r colour and i-band surface brightness), a multidimensional quadratic model yields M_{H I}/{{M}_{*}} scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that g - r colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with M_{H I}/{{M}_{*}}. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (PR) and the scatter in the training procedure (σfit), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the M_{H I}/{{M}_{*}} estimation. In contrast to previous works, our M_{H I}/{{M}_{*}} estimation has no systematic trend with galactic parameters such as M⋆, g - r and star formation rate. We present a catalogue of M_{H I}/{{M}_{*}} estimates for more than half a million galaxies in the SDSS, of which ˜150 000 galaxies have a secure selection parameter with average scatter in the M_{H I}/{{M}_{*}} estimation of 0.22 dex.

  17. Artificial intelligence tools for pattern recognition

    NASA Astrophysics Data System (ADS)

    Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

    2017-06-01

    In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

  18. Pattern recognition and control in manipulation

    NASA Technical Reports Server (NTRS)

    Bejczy, A. K.; Tomovic, R.

    1976-01-01

    A new approach to the use of sensors in manipulator or robot control is discussed. The concept addresses the problem of contact or near-contact type of recognition of three-dimensional forms of objects by proprioceptive and/or exteroceptive sensors integrated with the terminal device. This recognition of object shapes both enhances and simplifies the automation of object handling. Several examples have been worked out for the 'Belgrade hand' and for a parallel jaw terminal device, both equipped with proprioceptive (position) and exteroceptive (proximity) sensors. The control applications are discussed in the framework of a multilevel man-machine system control. The control applications create interesting new issues which, in turn, invite novel theoretical considerations. An important issue is the problem of stability in control when the control is referenced to patterns.

  19. Automated Three-Dimensional Microbial Sensing and Recognition Using Digital Holography and Statistical Sampling

    PubMed Central

    Moon, Inkyu; Yi, Faliu; Javidi, Bahram

    2010-01-01

    We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition. PMID:22163664

  20. Automated three-dimensional microbial sensing and recognition using digital holography and statistical sampling.

    PubMed

    Moon, Inkyu; Yi, Faliu; Javidi, Bahram

    2010-01-01

    We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition.

  1. Digital signal processing algorithms for automatic voice recognition

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1987-01-01

    The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

  2. Cascaded linear shift-invariant processors in optical pattern recognition.

    PubMed

    Reed, S; Coupland, J

    2001-08-10

    We study a cascade of linear shift-invariant processing modules (correlators), each augmented with a nonlinear threshold as a means to increase the performance of high-speed optical pattern recognition. This configuration is a special class of multilayer, feed-forward neural networks and has been proposed in the literature as a relatively fast best-guess classifier. However, it seems that, although cascaded correlation has been proposed in a number of specific pattern recognition problems, the importance of the configuration has been largely overlooked. We prove that the cascaded architecture is the exact structure that must be adopted if a multilayer feed-forward neural network is trained to produce a shift-invariant output. In contrast with more generalized multilayer networks, the approach is easily implemented in practice with optical techniques and is therefore ideally suited to the high-speed analysis of large images. We have trained a digital model of the system using a modified backpropagation algorithm with optimization using simulated annealing techniques. The resulting cascade has been applied to a defect recognition problem in the canning industry as a benchmark for comparison against a standard linear correlation filter, the minimum average correlation energy (MACE) filter. We show that the nonlinear performance of the cascade is a significant improvement over that of the linear MACE filter in this case.

  3. Statistical pattern recognition algorithms for autofluorescence imaging

    NASA Astrophysics Data System (ADS)

    Kulas, Zbigniew; Bereś-Pawlik, Elżbieta; Wierzbicki, Jarosław

    2009-02-01

    In cancer diagnostics the most important problems are the early identification and estimation of the tumor growth and spread in order to determine the area to be operated. The aim of the work was to design of statistical algorithms helping doctors to objectively estimate pathologically changed areas and to assess the disease advancement. In the research, algorithms for classifying endoscopic autofluorescence images of larynx and intestine were used. The results show that the statistical pattern recognition offers new possibilities for endoscopic diagnostics and can be of a tremendous help in assessing the area of the pathological changes.

  4. Interpretation techniques. [image enhancement and pattern recognition

    NASA Technical Reports Server (NTRS)

    Dragg, J. L.

    1974-01-01

    The image enhancement and geometric correction and registration techniques developed and/or demonstrated on ERTS data are relatively mature and greatly enhance the utility of the data for a large variety of users. Pattern recognition was improved by the use of signature extension, feature extension, and other classification techniques. Many of these techniques need to be developed and generalized to become operationally useful. Advancements in the mass precision processing of ERTS were demonstrated, providing the hope for future earth resources data to be provided in a more readily usable state. Also in evidence is an increasing and healthy interaction between the techniques developers and the user/applications investigators.

  5. Syntactic Pattern Recognition Approach To Scene Matching

    NASA Astrophysics Data System (ADS)

    Gilmore, John F.

    1983-03-01

    This paper describes a technique for matching two images containing natural terrain and tactical objects using syntactic pattern recognition. A preprocessor analyzes each image to identify potential areas of interest. Points of interest in an image are classified and a graph possessing properties of invariance is created based on these points. Classification derived grammar strings are generated for each classified graph structure. A local match analysis is performed and the best global match is constructed. A probability-of-match metric is computed in order to evaluate the global match. Examples demonstrating these steps are provided and actual FLIR image results are shown.

  6. Interpretation techniques. [image enhancement and pattern recognition

    NASA Technical Reports Server (NTRS)

    Dragg, J. L.

    1974-01-01

    The image enhancement and geometric correction and registration techniques developed and/or demonstrated on ERTS data are relatively mature and greatly enhance the utility of the data for a large variety of users. Pattern recognition was improved by the use of signature extension, feature extension, and other classification techniques. Many of these techniques need to be developed and generalized to become operationally useful. Advancements in the mass precision processing of ERTS were demonstrated, providing the hope for future earth resources data to be provided in a more readily usable state. Also in evidence is an increasing and healthy interaction between the techniques developers and the user/applications investigators.

  7. Pattern recognition in hyperspectral persistent imaging

    NASA Astrophysics Data System (ADS)

    Rosario, Dalton; Romano, Joao; Borel, Christoph

    2015-05-01

    We give updates on a persistent imaging experiment dataset, being considered for public release in a foreseeable future, and present additional observations analyzing a subset of the dataset. The experiment is a long-term collaborative effort among the Army Research Laboratory, Army Armament RDEC, and Air Force Institute of Technology that focuses on the collection and exploitation of longwave infrared (LWIR) hyperspectral imagery. We emphasize the inherent challenges associated with using remotely sensed LWIR hyperspectral imagery for material recognition, and show that this data type violates key data assumptions conventionally used in the scientific community to develop detection/ID algorithms, i.e., normality, independence, identical distribution. We treat LWIR hyperspectral imagery as Longitudinal Data and aim at proposing a more realistic framework for material recognition as a function of spectral evolution through time, and discuss limitations. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. But through a longitudinal analysis of a fixed site with multiple material types, we quantify and argue that, as data evolve through a full diurnal cycle, pattern recognition problems are longitudinal in nature and that by applying this knowledge may lead to better algorithms.

  8. Pattern recognition receptors in microbial keratitis

    PubMed Central

    Taube, M-A; del Mar Cendra, M; Elsahn, A; Christodoulides, M; Hossain, P

    2015-01-01

    Microbial keratitis is a significant cause of global visual impairment and blindness. Corneal infection can be caused by a wide variety of pathogens, each of which exhibits a range of mechanisms by which the immune system is activated. The complexity of the immune response to corneal infection is only now beginning to be elucidated. Crucial to the cornea's defences are the pattern-recognition receptors: Toll-like and Nod-like receptors and the subsequent activation of inflammatory pathways. These inflammatory pathways include the inflammasome and can lead to significant tissue destruction and corneal damage, with the potential for resultant blindness. Understanding the immune mechanisms behind this tissue destruction may enable improved identification of therapeutic targets to aid development of more specific therapies for reducing corneal damage in infectious keratitis. This review summarises current knowledge of pattern-recognition receptors and their downstream pathways in response to the major keratitis-causing organisms and alludes to potential therapeutic approaches that could alleviate corneal blindness. PMID:26160532

  9. The effect of filtering and inter-digit interval on the recognition of dichotic digits.

    PubMed

    Carter, A S; Wilson, R H

    2000-01-01

    The new compact disc from the Department of Veterans Affairs, Tonal and Speech Materials for Auditory, Perceptual Assessment, Disc 2.0 (1998), contains two lists of randomly interleaved 1-, 2-, and 3-pair dichotic digits. Two experiments are reported, in which the effects of low-pass filtering and inter-digit interval on dichotic digit recognition were investigated in adult listeners with normal hearing and with mild-to-moderate cochlear hearing loss. Results demonstrated that in the filtered condition, as the low-pass cutoff was increased, there was an increase in recognition performance for 1-, 2-, and 3-pair dichotic digits. When compared to normative data for the materials, findings indicate that the interleaved 1-, 2-, and 3-pair dichotic digit materials were essentially resistant to the effects of hearing loss. There was no significant change in recognition performance as a function of inter-digit interval. The studied 625-ms range of inter-digit intervals studied produced consistent recognition performance with both groups of listeners.

  10. Comparison of eye imaging pattern recognition using neural network

    NASA Astrophysics Data System (ADS)

    Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

    2015-05-01

    The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

  11. Pattern recognition, neural networks, and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Bezdek, James C.

    1991-03-01

    We write about the relationship between numerical patten recognition and neural-like computation networks. Extensive research that proposes the use of neural models for a wide variety of applications has been conducted in the past few years. Sometimes justification for investigating the potential of neural nets (NNs) is obvious. On the other hand, current enthusiasm for this approach has also led to the use of neural models when the apparent rationale for their use has been justified by what is best described as 'feeding frenzy'. In this latter instance there is at times concomitant lack of concern about many 'side issues' connected with algorithms (e.g., complexity, convergence, stability, robustness and performance validation) that need attention before any computational model becomes part of an operation system. These issues are examined with a view towards guessing how best to integrate and exploit the promise of the neural approach with there efforts aimed at advancing the art and science of pattern recognition and its applications in fielded systems in the next decade.

  12. A neural network for visual pattern recognition

    SciTech Connect

    Fukushima, K.

    1988-03-01

    A modeling approach, which is a synthetic approach using neural network models, continues to gain importance. In the modeling approach, the authors study how to interconnect neurons to synthesize a brain model, which is a network with the same functions and abilities as the brain. The relationship between modeling neutral networks and neurophysiology resembles that between theoretical physics and experimental physics. Modeling takes synthetic approach, while neurophysiology or psychology takes an analytical approach. Modeling neural networks is useful in explaining the brain and also in engineering applications. It brings the results of neurophysiological and psychological research to engineering applications in the most direct way possible. This article discusses a neural network model thus obtained, a model with selective attention in visual pattern recognition.

  13. FPGA design of correlation-based pattern recognition

    NASA Astrophysics Data System (ADS)

    Jridi, Maher; Alfalou, Ayman

    2017-05-01

    Optical/Digital pattern recognition and tracking based on optical/digital correlation are a well-known techniques to detect, identify and localize a target object in a scene. Despite the limited number of treatments required by the correlation scheme, computational time and resources are relatively high. The most computational intensive treatment required by the correlation is the transformation from spatial to spectral domain and then from spectral to spatial domain. Furthermore, these transformations are used on optical/digital encryption schemes like the double random phase encryption (DRPE). In this paper, we present a VLSI architecture for the correlation scheme based on the fast Fourier transform (FFT). One interesting feature of the proposed scheme is its ability to stream image processing in order to perform correlation for video sequences. A trade-off between the hardware consumption and the robustness of the correlation can be made in order to understand the limitations of the correlation implementation in reconfigurable and portable platforms. Experimental results obtained from HDL simulations and FPGA prototype have demonstrated the advantages of the proposed scheme.

  14. Playing tag with ANN: boosted top identification with pattern recognition

    NASA Astrophysics Data System (ADS)

    Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

    2015-07-01

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a "digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  15. Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models

    PubMed Central

    Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

    2016-01-01

    A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner’s faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals. PMID:27191162

  16. Pattern recognition with magnonic holographic memory device

    SciTech Connect

    Kozhevnikov, A.; Dudko, G.; Filimonov, Y.; Gertz, F.; Khitun, A.

    2015-04-06

    In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed.

  17. Pattern recognition with magnonic holographic memory device

    NASA Astrophysics Data System (ADS)

    Kozhevnikov, A.; Gertz, F.; Dudko, G.; Filimonov, Y.; Khitun, A.

    2015-04-01

    In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed.

  18. Identical and Reverse Visual Pattern Recognition in Deaf Children.

    ERIC Educational Resources Information Center

    Bragman, Ruth; Hardy, Robert

    1979-01-01

    Describes a study that investigated the development of pattern recognition and pattern reversal in 20 deaf children aged six through eight and its relation to age of exposure to a gestural symbol system. (Author/DS)

  19. Predictable Pattern Digital Artery Perforator Flap

    PubMed Central

    Epameinondas, Kostopoulos; Christos, Agiannidis; Petros, Konofaos; Avraam, Dounavis; Othon, Papadopoulos; Vincent, Casoli

    2016-01-01

    Background: The proper digital arteries as any other axial vessel give rise to multiple cutaneous perforators either volar or dorsal along their course. Their identification is performed with Doppler flowmetry. The vasculo-cutano-tendino-osseous complex (VCTOC), which was described by the senior authors, was responsible for the vascularization of all digital anatomic structures (extensor apparatus, skin, periosteum). Their consistent appearance to well measured distances from the digital joints led the way to the present clinical study for highlighting this described anatomy in-vivo and demonstrating the predictability in digital artery perforator (DAP) flap harvest. Methods: From November 2012 to March 2014, fifteen patients underwent reconstruction with a predictable pattern digital artery perforator flap (PPDAP), based on the previously described VCTOC mapping, for digital lesions secondary to tumor extirpation. Flaps were designed as V-Y advancement or propeller type. Postoperative control concerned flap viability and digital function. Results: Seven males and 8 females underwent elective surgery using PPDAP flaps for digital defects following tumor extirpation. The diameter of the defect ranged from 0.5 to 1.5 cm. The vast majority of the lesions were identified on the right hand, the index, the ring finger and the distal phalanx. All flaps survived without signs of venous congestion. No functional digital problems were observed during follow up (mean of 77 months). A minor wound dehiscence presented in one patient. Conclusions: Authors introduced the concept of a “predictable pattern” in the surgery of perforator flaps in the digits. These flaps are reliable and could be a valuable reconstructive option. PMID:27418896

  20. Pattern recognition: A basis for remote sensing data analysis

    NASA Technical Reports Server (NTRS)

    Swain, P. H.

    1973-01-01

    The theoretical basis for the pattern-recognition-oriented algorithms used in the multispectral data analysis software system is discussed. A model of a general pattern recognition system is presented. The receptor or sensor is usually a multispectral scanner. For each ground resolution element the receptor produces n numbers or measurements corresponding to the n channels of the scanner.

  1. Image pattern recognition supporting interactive analysis and graphical visualization

    NASA Technical Reports Server (NTRS)

    Coggins, James M.

    1992-01-01

    Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

  2. Online and Offline Pattern Recognition in PANDA

    NASA Astrophysics Data System (ADS)

    Boca, Gianluigi

    2016-11-01

    PANDA is one of the four experiments that will run at the new facility FAIR that is being built in Darmstadt, Germany. It is a fixed target experiment: a beam of antiprotons collides on a jet proton target (the maximum center of mass energy is 5.46 GeV). The interaction rate at the startup will be 2MHz with the goal of reaching 20MHz at full luminosity. The beam of antiprotons will be essentially continuous. PANDA will have NO hardware trigger but only a software trigger, to allow for maximum flexibility in the physics program. All those characteristics are severe challenges for the reconstruction code that 1) must be fast, since it has to be validated up to 20MHz interaction rate; 2) must be able to reject fake tracks caused by the remnant hits, belonging to previous or later events in some slow detectors, for example the straw tubes in the central region. The Pattern Recognition (PR) of PANDA will have to run both online to achieve a first fast selection, and offline, at lower rate, for a more refined selection. In PANDA the PR code is continuously evolving; this contribution shows the present status. I will give an overview of three examples of PR following different strategies and/or implemented on different hardware (FPGA, GPUs, CPUs) and, when available, I will report the performances.

  3. Pattern-Recognition System for Approaching a Known Target

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance; Cheng, Yang

    2008-01-01

    A closed-loop pattern-recognition system is designed to provide guidance for maneuvering a small exploratory robotic vehicle (rover) on Mars to return to a landed spacecraft to deliver soil and rock samples that the spacecraft would subsequently bring back to Earth. The system could be adapted to terrestrial use in guiding mobile robots to approach known structures that humans could not approach safely, for such purposes as reconnaissance in military or law-enforcement applications, terrestrial scientific exploration, and removal of explosive or other hazardous items. The system has been demonstrated in experiments in which the Field Integrated Design and Operations (FIDO) rover (a prototype Mars rover equipped with a video camera for guidance) is made to return to a mockup of Mars-lander spacecraft. The FIDO rover camera autonomously acquires an image of the lander from a distance of 125 m in an outdoor environment. Then under guidance by an algorithm that performs fusion of multiple line and texture features in digitized images acquired by the camera, the rover traverses the intervening terrain, using features derived from images of the lander truss structure. Then by use of precise pattern matching for determining the position and orientation of the rover relative to the lander, the rover aligns itself with the bottom of ramps extending from the lander, in preparation for climbing the ramps to deliver samples to the lander. The most innovative aspect of the system is a set of pattern-recognition algorithms that govern a three-phase visual-guidance sequence for approaching the lander. During the first phase, a multifeature fusion algorithm integrates the outputs of a horizontal-line-detection algorithm and a wavelet-transform-based visual-area-of-interest algorithm for detecting the lander from a significant distance. The horizontal-line-detection algorithm is used to determine candidate lander locations based on detection of a horizontal deck that is part of the

  4. Pattern-Recognition Receptors and Gastric Cancer

    PubMed Central

    Castaño-Rodríguez, Natalia; Kaakoush, Nadeem O.; Mitchell, Hazel M.

    2014-01-01

    Chronic inflammation has been associated with an increased risk of several human malignancies, a classic example being gastric adenocarcinoma (GC). Development of GC is known to result from infection of the gastric mucosa by Helicobacter pylori, which initially induces acute inflammation and, in a subset of patients, progresses over time to chronic inflammation, gastric atrophy, intestinal metaplasia, dysplasia, and finally intestinal-type GC. Germ-line encoded receptors known as pattern-recognition receptors (PRRs) are critical for generating mature pro-inflammatory cytokines that are crucial for both Th1 and Th2 responses. Given that H. pylori is initially targeted by PRRs, it is conceivable that dysfunction within genes of this arm of the immune system could modulate the host response against H. pylori infection, and subsequently influence the emergence of GC. Current evidence suggests that Toll-like receptors (TLRs) (TLR2, TLR3, TLR4, TLR5, and TLR9), nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) (NOD1, NOD2, and NLRP3), a C-type lectin receptor (DC-SIGN), and retinoic acid-inducible gene (RIG)-I-like receptors (RIG-I and MDA-5), are involved in both the recognition of H. pylori and gastric carcinogenesis. In addition, polymorphisms in genes involved in the TLR (TLR1, TLR2, TLR4, TLR5, TLR9, and CD14) and NLR (NOD1, NOD2, NLRP3, NLRP12, NLRX1, CASP1, ASC, and CARD8) signaling pathways have been shown to modulate the risk of H. pylori infection, gastric precancerous lesions, and/or GC. Further, the modulation of PRRs has been suggested to suppress H. pylori-induced inflammation and enhance GC cell apoptosis, highlighting their potential relevance in GC therapeutics. In this review, we present current advances in our understanding of the role of the TLR and NLR signaling pathways in the pathogenesis of GC, address the involvement of other recently identified PRRs in GC, and discuss the potential implications of PRRs in GC immunotherapy

  5. Searching for pulsars using image pattern recognition

    SciTech Connect

    Zhu, W. W.; Berndsen, A.; Madsen, E. C.; Tan, M.; Stairs, I. H.; Brazier, A.; Lazarus, P.; Lynch, R.; Scholz, P.; Stovall, K.; Cohen, S.; Dartez, L. P.; Lunsford, G.; Martinez, J. G.; Mata, A.; Ransom, S. M.; Banaszak, S.; Biwer, C. M.; Flanigan, J.; Rohr, M. E-mail: berndsen@phas.ubc.ca; and others

    2014-02-01

    In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The

  6. Searching for Pulsars Using Image Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Zhu, W. W.; Berndsen, A.; Madsen, E. C.; Tan, M.; Stairs, I. H.; Brazier, A.; Lazarus, P.; Lynch, R.; Scholz, P.; Stovall, K.; Ransom, S. M.; Banaszak, S.; Biwer, C. M.; Cohen, S.; Dartez, L. P.; Flanigan, J.; Lunsford, G.; Martinez, J. G.; Mata, A.; Rohr, M.; Walker, A.; Allen, B.; Bhat, N. D. R.; Bogdanov, S.; Camilo, F.; Chatterjee, S.; Cordes, J. M.; Crawford, F.; Deneva, J. S.; Desvignes, G.; Ferdman, R. D.; Freire, P. C. C.; Hessels, J. W. T.; Jenet, F. A.; Kaplan, D. L.; Kaspi, V. M.; Knispel, B.; Lee, K. J.; van Leeuwen, J.; Lyne, A. G.; McLaughlin, M. A.; Siemens, X.; Spitler, L. G.; Venkataraman, A.

    2014-02-01

    In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The

  7. Pattern recognition in the database of a mask layout

    NASA Astrophysics Data System (ADS)

    Chen, Shih-Ying; Lynn, Eric C.; Shin, Jaw-Jung

    2002-07-01

    The request of pattern recognition has been frequently brought up by both mask and wafer engineers. Despite different intentions, pattern recognition is usually the first step of many applications and hence plays a major role to accomplish certain tasks. For the purpose of this work, pattern recognition is defined as searching a specific polygon or a group of particular patterns from a chip layout. Operator scan is truly not an efficient approach of pattern recognition, in particular, for cases with huge design database of advanced semiconductor integrated circuits. Obviously, an automation system of pattern recognition is necessary and benefits the data preparation process. Two categories of pattern recognition are discussed in the present study, 'fuzzy search' and 'exact match.' Each category has its own application, but the searching algorithms could be much different. Details of searching algorithms are given for both categories of pattern recognition. Due to the nature of industrial standard, the scope of the present application is limited to database with GDSII format. Hence, coordinate searching is internally used inside the searching engine.

  8. Signatures analysis and recognition of severe weather patterns

    NASA Technical Reports Server (NTRS)

    Wang, P. P.; Burns, R. C.

    1975-01-01

    The feasibility of designing a prediction and warning system for severe weather conditions on the basis of time series analysis and pattern recognition is examined. Data accumulated by Taylor (1972) on the rate of atmospherics produced by severe, tornado-producing storms that struck Oklahoma City during April 1970 are analyzed by time series analysis and pattern recognition. Power spectra, cross-power spectra, coherence functions, and time-varying patterns are analyzed.

  9. Signatures analysis and recognition of severe weather patterns

    NASA Technical Reports Server (NTRS)

    Wang, P. P.; Burns, R. C.

    1975-01-01

    The feasibility of designing a prediction and warning system for severe weather conditions on the basis of time series analysis and pattern recognition is examined. Data accumulated by Taylor (1972) on the rate of atmospherics produced by severe, tornado-producing storms that struck Oklahoma City during April 1970 are analyzed by time series analysis and pattern recognition. Power spectra, cross-power spectra, coherence functions, and time-varying patterns are analyzed.

  10. PATTERN RECOGNITION AND CLASSIFICATION USING ADAPTIVE LINEAR NEURON DEVICES

    DTIC Science & Technology

    adaption by an adaptive linear neuron ( Adaline ), as applied to the pattern recognition and classification problem; (2) Four possible iterative adaption...schemes which may be used to train as Adaline ; (3) Use of Multiple Adalines (Madaline) and two logic layers to increase system capability; and (4) Use...of Adaline in the practical fields of Speech Recognition, Weather Forecasting and Adaptive Control Systems and the possible use of Madaline in the Character Recognition field.

  11. Classification and machine recognition of severe weather patterns

    NASA Technical Reports Server (NTRS)

    Wang, P. P.; Burns, R. C.

    1976-01-01

    Forecasting and warning of severe weather conditions are treated from the vantage point of pattern recognition by machine. Pictorial patterns and waveform patterns are distinguished. Time series data on sferics are dealt with by considering waveform patterns. A severe storm patterns recognition machine is described, along with schemes for detection via cross-correlation of time series (same channel or different channels). Syntactic and decision-theoretic approaches to feature extraction are discussed. Active and decayed tornados and thunderstorms, lightning discharges, and funnels and their related time series data are studied.

  12. Macrophage pattern recognition receptors in immunity, homeostasis and self tolerance.

    PubMed

    Mukhopadhyay, Subhankar; Plüddemann, Annette; Gordon, Siamon

    2009-01-01

    Macrophages, a major component of innate immune defence, express a large repertoire of different classes of pattern recognition receptors and other surface antigens which determine the immunologic and homeostatic potential of these versatile cells. In the light of present knowledge ofmacrophage surface antigens, we discuss self versus nonself recognition, microbicidal effector functions and self tolerance in the innate immune system.

  13. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

    Osbourn, G.C.; Martinez, R.F.

    1999-05-04

    A method of clustering using a novel template to define a region of influence is disclosed. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques. 30 figs.

  14. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    1999-01-01

    A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  15. Proceedings of the NASA/MPRIA Workshop: Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr.

    1983-01-01

    Outlines of talks presented at the workshop conducted at Texas A & M University on February 3 and 4, 1983 are presented. Emphasis was given to the application of Mathematics to image processing and pattern recognition.

  16. Development of Pattern Recognition in Infant Pigtailed Macaques (Macaca nemestrina).

    ERIC Educational Resources Information Center

    Gunderson, Virginia M.; Sackett, Gene P.

    1984-01-01

    Examined the development of pattern recognition in infant pigtailed macaques using the familiarization novelty technique. Results indicate that by at least 200 days postconception subjects show a consistently reliable visual response to novelty. (Author/RH)

  17. Development of Pattern Recognition in Infant Pigtailed Macaques (Macaca nemestrina).

    ERIC Educational Resources Information Center

    Gunderson, Virginia M.; Sackett, Gene P.

    1984-01-01

    Examined the development of pattern recognition in infant pigtailed macaques using the familiarization novelty technique. Results indicate that by at least 200 days postconception subjects show a consistently reliable visual response to novelty. (Author/RH)

  18. Visual cluster analysis and pattern recognition template and methods

    SciTech Connect

    Osbourn, G.C.; Martinez, R.F.

    1993-12-31

    This invention is comprised of a method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  19. Photonic correlator pattern recognition: Application to autonomous docking

    NASA Technical Reports Server (NTRS)

    Sjolander, Gary W.

    1991-01-01

    Optical correlators for real-time automatic pattern recognition applications have recently become feasible due to advances in high speed devices and filter formulation concepts. The devices are discussed in the context of their use in autonomous docking.

  20. The Pandora software development kit for pattern recognition

    NASA Astrophysics Data System (ADS)

    Marshall, J. S.; Thomson, M. A.

    2015-09-01

    The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora software development kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e+e- linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber.

  1. Hopfield's Model of Patterns Recognition and Laws of Artistic Perception

    NASA Astrophysics Data System (ADS)

    Yevin, Igor; Koblyakov, Alexander

    The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

  2. Influence of language experience on digit recognition by English and Chinese listeners.

    PubMed

    Zhou, Xiaoqing; Yuan, Wei; Galvin, John J; Fu, Qian-Jie; Zhang, Ying

    2015-09-01

    Digit recognition was measured in quiet and in two noise conditions by English-native (EN) and Chinese-native (CN) listeners. EN listeners were tested using English digits and CN listeners were tested using both English and Chinese digits. In quiet, forward digit span recall worsened for both groups as the number of digits was increased. Significant effects of language experience were observed with five or more digits. Language experience had a significant effect on digit recognition in babble but not in steady noise. These results suggest that understanding of a nonnative language can be influenced by both cognitive load and listening environment.

  3. Face recognition system and method using face pattern words and face pattern bytes

    DOEpatents

    Zheng, Yufeng

    2014-12-23

    The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

  4. Digital authentication with copy-detection patterns

    NASA Astrophysics Data System (ADS)

    Picard, Justin

    2004-06-01

    Technologies for making high-quality copies of documents are getting more available, cheaper, and more efficient. As a result, the counterfeiting business engenders huge losses, ranging to 5% to 8% of worldwide sales of brand products, and endangers the reputation and value of the brands themselves. Moreover, the growth of the Internet drives the business of counterfeited documents (fake IDs, university diplomas, checks, and so on), which can be bought easily and anonymously from hundreds of companies on the Web. The incredible progress of digital imaging equipment has put in question the very possibility of verifying the authenticity of documents: how can we discern genuine documents from seemingly "perfect" copies? This paper proposes a solution based on creating digital images with specific properties, called a Copy-detection patterns (CDP), that is printed on arbitrary documents, packages, etc. CDPs make an optimal use of an "information loss principle": every time an imae is printed or scanned, some information is lost about the original digital image. That principle applies even for the highest quality scanning, digital imaging, printing or photocopying equipment today, and will likely remain true for tomorrow. By measuring the amount of information contained in a scanned CDP, the CDP detector can take a decision on the authenticity of the document.

  5. Pattern recognition and active vision in chickens.

    PubMed

    Dawkins, M S; Woodington, A

    2000-02-10

    Recognition of objects or environmental landmarks is problematic because appearance can vary widely depending on illumination, viewing distance, angle of view and so on. Storing a separate image or 'template' for every possible view requires vast numbers to be stored and scanned, has a high probability of recognition error and appears not to be the solution adopted by primates. However, some invertebrate template matching systems can achieve recognition by 'active vision' in which the animal's own behaviour is used to achieve a fit between template and object, for example by repeatedly following a set path. Recognition is thus limited to views from the set path but achieved with a minimal number of templates. Here we report the first evidence of similar active vision in a bird, in the form of locomotion and individually distinct head movements that give the eyes a similar series of views on different occasions. The hens' ability to recognize objects is also found to decrease when their normal paths are altered.

  6. Patterns recognition of electric brain activity using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  7. Hybrid Neural Network for Pattern Recognition.

    DTIC Science & Technology

    1997-02-03

    two one-layer neural networks and the second stage comprises a feedforward two-layer neural network . A method for recognizing patterns is also...topological representations of the input patterns using the first and second neural networks. The method further comprises providing a third neural network for...classifying and recognizing the inputted patterns and training the third neural network with a back-propagation algorithm so that the third neural network recognizes at least one interested pattern.

  8. Large-area settlement pattern recognition from Landsat-8 data

    NASA Astrophysics Data System (ADS)

    Wieland, Marc; Pittore, Massimiliano

    2016-09-01

    The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties.

  9. Detection and recognition of angular frequency patterns.

    PubMed

    Wilson, Hugh R; Propp, Roni

    2015-05-01

    Previous research has extensively explored visual encoding of smoothly curved, closed contours described by sinusoidal variation of pattern radius as a function of polar angle (RF patterns). Although the contours of many biologically significant objects are curved, we also confront shapes with a more jagged and angular appearance. To study these, we introduce here a novel class of visual stimuli that deform smoothly from a circle to an equilateral polygon with N sides (AF patterns). Threshold measurements reveal that both AF and RF patterns can be discriminated from circles at the same deformation amplitude, approximately 18.0arcsec, which is in the hyperacuity range. Thresholds were slightly higher for patterns with 3.0 cycles than for those with 5.0 cycles. Discrimination between AF and RF patterns was 75% correct at an amplitude that was approximately 3.0 times the threshold amplitude, which implies that AF and RF patterns activate different neural populations. Experiments with jittered patterns in which the contour was broken into several pieces and shifted inward or outward had much less effect on AF patterns than on RF patterns. Similarly, thresholds for single angles of AF patterns showed no significant difference from thresholds for the entire AF pattern. Taken together, these results imply that the visual system incorporates angles explicitly in the representation of closed object contours, but it suggests that angular contours are represented more locally than are curved contours.

  10. Escherichia coli O157:H7 restriction pattern recognition by artificial neural network.

    PubMed Central

    Carson, C A; Keller, J M; McAdoo, K K; Wang, D; Higgins, B; Bailey, C W; Thorne, J G; Payne, B J; Skala, M; Hahn, A W

    1995-01-01

    An artificial neural network model for the recognition of Escherichia coli O157:H7 restriction patterns was designed. In the training phase, images of two classes of E. coli isolates (O157:H7 and non-O157:H7) were digitized and transmitted to the neural network. The system was then tested for recognition of images not included in the training set. Promising results were achieved with the designed network configuration, providing a basis for further study. This application of a new generation of computation technology serves as an example of its usefulness in microbiology. PMID:8576341

  11. Pattern recognition of internal structural defects in industrial radiographic testing based on neural network

    NASA Astrophysics Data System (ADS)

    Ming, Ming; Li, Zheng

    2001-09-01

    It is shown that an artificial neural network can be used to classify internal structural defects in radiographic nondestructive testing. We design a series of images presenting phantoms to simulate three different classes of defects: porosity, crack, and slag. Features of these defects are selected from domains of geometry, gray statistics, frequency spectrum, and etc. Some of them are especially suitable for pattern recognition in the case of radiographic image. A three-layered neural network trained with back-propagation rule is developed to carry out the classification. The training and testing data for the net are the features extracted from digitized radiographic images. Results are presented with satisfactory recognition rate.

  12. Recognition of control chart patterns with incomplete samples

    NASA Astrophysics Data System (ADS)

    Miftah Abdelrahman Senoussi, Ahmed; Masood, Ibrahim; Nasrull Abdol Rahman, Mohd; Fahrul Hassan, Mohd

    2017-08-01

    In quality control, automated recognition of statistical process control (SPC) chart patterns is an effective technique for monitoring unnatural variation (UV) in manufacturing process. In most studies, focus was given on complete patterns by assuming there is no constrain in the SPC samples. Nevertheless, there is in-practice case whereby the SPC samples cannot be captured properly due to measurement sensor error or human error. Thus, this research aims to design a recognition scheme for incomplete samples pattern that will be useful for an industrial application. The design methodology involves three phases: (i) simulation of UV and SPC chart patterns, (ii) design of pattern recognition scheme, and (iii) evaluation of performance recognition. It involves modelling of the simulated SPC samples in bivariate quality control, raw data input representation, and recognizer training and testing. The proposed technique indicates a high recognition accuracy (normal pattern = 99.5%, shift patterns = 97.5%). This research will provide a new perspective in SPC charting scheme when dealing with constraint in terms of incomplete samples, which is greatly useful for an industrial practitioner in finding the solution for corrective action.

  13. Learning optimal features for visual pattern recognition

    NASA Astrophysics Data System (ADS)

    Labusch, Kai; Siewert, Udo; Martinetz, Thomas; Barth, Erhardt

    2007-02-01

    The optimal coding hypothesis proposes that the human visual system has adapted to the statistical properties of the environment by the use of relatively simple optimality criteria. We here (i) discuss how the properties of different models of image coding, i.e. sparseness, decorrelation, and statistical independence are related to each other (ii) propose to evaluate the different models by verifiable performance measures (iii) analyse the classification performance on images of handwritten digits (MNIST data base). We first employ the SPARSENET algorithm (Olshausen, 1998) to derive a local filter basis (on 13 × 13 pixels windows). We then filter the images in the database (28 × 28 pixels images of digits) and reduce the dimensionality of the resulting feature space by selecting the locally maximal filter responses. We then train a support vector machine on a training set to classify the digits and report results obtained on a separate test set. Currently, the best state-of-the-art result on the MNIST data base has an error rate of 0,4%. This result, however, has been obtained by using explicit knowledge that is specific to the data (elastic distortion model for digits). We here obtain an error rate of 0,55% which is second best but does not use explicit data specific knowledge. In particular it outperforms by far all methods that do not use data-specific knowledge.

  14. Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

    PubMed

    Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

    2017-05-04

    The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Postprocessing for character recognition using pattern features and linguistic information

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

    1993-04-01

    We propose a new method of post-processing for character recognition using pattern features and linguistic information. This method corrects errors in the recognition of handwritten Japanese sentences containing Kanji characters. This post-process method is characterized by having two types of character recognition. Improving the accuracy of the character recognition rate of Japanese characters is made difficult by the large number of characters, and the existence of characters with similar patterns. Therefore, it is not practical for a character recognition system to recognize all characters in detail. First, this post-processing method generates a candidate character table by recognizing the simplest features of characters. Then, it selects words corresponding to the character from the candidate character table by referring to a word and grammar dictionary before selecting suitable words. If the correct character is included in the candidate character table, this process can correct an error, however, if the character is not included, it cannot correct an error. Therefore, if this method can presume a character does not exist in a candidate character table by using linguistic information (word and grammar dictionary). It then can verify a presumed character by character recognition using complex features. When this method is applied to an online character recognition system, the accuracy of character recognition improves 93.5% to 94.7%. This proved to be the case when it was used for the editorials of a Japanese newspaper (Asahi Shinbun).

  16. Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

    NASA Astrophysics Data System (ADS)

    Fernández, Ariel; Ferrari, José A.

    2017-05-01

    Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

  17. A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation

    USDA-ARS?s Scientific Manuscript database

    Perception of pathogen-associated molecular patterns (PAMPs) by surface-localised pattern-recognition receptors (PRRs) is a key component of plant innate immunity. Most known plant PRRs are receptor kinases and initiation of PAMP-triggered immunity (PTI) signalling requires phosphorylation of the PR...

  18. Optimization of fuzzy logic analysis by diagonals for pattern recognition

    NASA Astrophysics Data System (ADS)

    Habiballa, Hashim; Hires, Matej

    2017-07-01

    The article presents an optimization of the fuzzy logic analysis method for pattern recognition. The enhancements of the original method through the usage of additional two types of pattern components - leftwise diagonal and rightwise diagonal ones. The method is described in theoretical background and further articles show the implementation and experimental verification of the approach.

  19. Associative Pattern Recognition Through Macro-molecular Self-Assembly

    NASA Astrophysics Data System (ADS)

    Zhong, Weishun; Schwab, David J.; Murugan, Arvind

    2017-05-01

    We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of N distinct molecular species. Similar to associative neural networks, the recognition here leverages dynamical attractors to recognize and reconstruct partially corrupted patterns. Traditional parameters of pattern recognition theory, such as sparsity, fidelity, and capacity are related to physical parameters, such as nucleation barriers, interaction range, and non-equilibrium assembly forces. Notably, we find that self-assembly bears greater similarity to continuous attractor neural networks, such as place cell networks that store spatial memories, rather than discrete memory networks. This relationship suggests that features and trade-offs seen here are not tied to details of self-assembly or neural network models but are instead intrinsic to associative pattern recognition carried out through short-ranged interactions.

  20. Face Recognition Using Local Quantized Patterns and Gabor Filters

    NASA Astrophysics Data System (ADS)

    Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

    2015-05-01

    The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

  1. Contour-based automatic crater recognition using digital elevation models from Chang'E missions

    NASA Astrophysics Data System (ADS)

    Zuo, Wei; Zhang, Zhoubin; Li, Chunlai; Wang, Rongwu; Yu, Linjie; Geng, Liang

    2016-12-01

    In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). Through DEM and image processing, this method can be used to reconstruct contour surfaces, extract and combine contour lines, set the characteristic parameters of crater morphology, and establish a crater pattern recognition program. The method has been tested and verified with DEM data from Chang'E-1 (CE-1) and Chang'E-2 (CE-2), showing a strong crater recognition ability with high detection rate, high robustness, and good adaptation to recognize various craters with different diameter and morphology. The method has been used to identify craters with high precision and accuracy on the Moon. The results meet requirements for supporting exploration and related scientific research for the Moon and planets.

  2. Pattern recognition of multiple objects using adaptive correlation filters

    NASA Astrophysics Data System (ADS)

    Pinedo-García, Marco I.; Kober, Vitaly

    2007-09-01

    A new method for reliable pattern recognition of multiple distorted objects in a cluttered background and consequent classification of the detected objects is proposed. The method is based on a bank of composite correlation filters. The filters are designed with the help of an iterative algorithm exploiting a modified version of synthetic discriminant functions. The bank consists of a minimal quantity of the filters required for a given input scene to guarantee a prespecified value of discrimination capability for pattern recognition and classification of all objects. Statistical analysis of the number of required correlations versus the recognition performance is provided and discussed. Computer simulation results obtained with the proposed method are compared with those of known techniques in terms of performance criteria for recognition and classification of objects.

  3. Three-dimensional-object recognition by use of single-exposure on-axis digital holography.

    PubMed

    Javidi, Bahram; Kim, Daesuk

    2005-02-01

    On-axis phase-shifting digital holography requires recording of multiple holograms. We describe a novel real-time three-dimensional- (3-D-) object recognition system that uses single-exposure on-axis digital holography. In contrast to 3-D-object recognition by means of a conventional phase-shifting scheme that requires multiple exposures, our proposed method requires only a single digital hologram to be synthesized and used to recognize 3-D objects. A benefit of the proposed 3-D recognition method is enhanced practicality of digital holography for 3-D recognition in terms of its simplicity and greater robustness to external scene parameters such as moving targets and environmental noise factors. We show experimentally the utility of the single-exposure on-axis digital holography-based 3-D-object recognition method.

  4. Pattern Recognition Approach to Neuropathy and Neuronopathy

    PubMed Central

    Barohn, Richard J; Amato, Anthony A.

    2014-01-01

    Synopsis Neuropathic disorders encompass those that affect the neuron’s cell body or neuronopathies, those affecting the peripheral process, or peripheral neuropathies. The peripheral neuropathies can be broadly subdivided into the myelinopathies and axonopathies. These conditions can be hereditary or acquired. Each of these disorders has distinct clinical features that enable neurologists to recognize the various patterns of presentation. Once a particular pattern is established, further laboratory studies can be performed to confirm the clinical impression. PMID:23642713

  5. A hybrid intelligence approach to artifact recognition in digital publishing

    NASA Astrophysics Data System (ADS)

    Vega-Riveros, J. Fernando; Santos Villalobos, Hector J.

    2006-02-01

    The system presented integrates rule-based and case-based reasoning for artifact recognition in Digital Publishing. In Variable Data Printing (VDP) human proofing could result prohibitive since a job could contain millions of different instances that may contain two types of artifacts: 1) evident defects, like a text overflow or overlapping 2) style-dependent artifacts, subtle defects that show as inconsistencies with regard to the original job design. We designed a Knowledge-Based Artifact Recognition tool for document segmentation, layout understanding, artifact detection, and document design quality assessment. Document evaluation is constrained by reference to one instance of the VDP job proofed by a human expert against the remaining instances. Fundamental rules of document design are used in the rule-based component for document segmentation and layout understanding. Ambiguities in the design principles not covered by the rule-based system are analyzed by case-based reasoning, using the Nearest Neighbor Algorithm, where features from previous jobs are used to detect artifacts and inconsistencies within the document layout. We used a subset of XSL-FO and assembled a set of 44 document samples. The system detected all the job layout changes, while obtaining an overall average accuracy of 84.56%, with the highest accuracy of 92.82%, for overlapping and the lowest, 66.7%, for the lack-of-white-space.

  6. A robust and biologically plausible spike pattern recognition network.

    PubMed

    Larson, Eric; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2010-11-17

    The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

  7. Bidirectional plasticity of cortical pattern recognition and behavioral sensory acuity

    PubMed Central

    Chapuis, Julie; Wilson, Donald A.

    2011-01-01

    Learning to adapt to a complex and fluctuating environment requires the ability to adjust neural representations of sensory stimuli. Through pattern completion processes, cortical networks can reconstruct familiar patterns from degraded input patterns, while pattern separation processes allow discrimination of even highly overlapping inputs. Here we show that the balance between pattern separation and completion is experience-dependent. Rats given extensive training with overlapping complex odorant mixtures show improved behavioral discrimination ability and enhanced cortical ensemble pattern separation. In contrast, behavioral training to disregard normally detectable differences between overlapping mixtures results in impaired cortical ensemble pattern separation (enhanced pattern completion) and impaired discrimination. This bidirectional effect was not found in the olfactory bulb, and may be due to plasticity within olfactory cortex itself. Thus pattern recognition, and the balance between pattern separation and completion, is highly malleable based on task demands and occurs in concert with changes in perceptual performance. PMID:22101640

  8. Recognition of one-, two-, and three-pair dichotic digits under free and directed recall.

    PubMed

    Strouse, A; Wilson, R H

    1999-01-01

    A one-, two-, and three-pair dichotic digit test was administered to 180 subjects (20-79 years) with varying degrees of hearing sensitivity. The test was carried out under free- and directed-recall response conditions. The results indicated that recognition performance decreased as a function of increasing age. Statistical analysis using analysis of covariance indicated that differences in performance between age groups were not owing to differences in hearing sensitivity. Thus, with the effects of hearing sensitivity removed, age alone accounted for a significant portion of the variability in the data among age groups. Normative data for the free-recall condition are reported for the two lists of randomly interleaved one-, two-, and three-pair digits included on the Tonal and Speech Materials for Auditory Perceptual Assessment, Disc 2.0 (1998) compact disc. A comparison of performance in free- and directed-recall conditions indicated three patterns of results. First, 58 percent of the subjects had normal recognition performance for one-, two-, and three-pair digits under both recall conditions. Second, 39 percent of the subjects had a deficit in the free-recall condition, with normal performance in the directed-recall condition, which was interpreted as representing primarily a cognitive problem. Third, the remaining 3 percent of the subjects showed a deficit in both free- and directed-recall conditions, which was attributed primarily to an auditory problem.

  9. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  10. Automatic pattern recognition in ECG time series.

    PubMed

    Sternickel, Karsten

    2002-05-01

    In this paper, a technique for the automatic detection of any recurrent pattern in ECG time series is introduced. The wavelet transform is used to obtain a multiresolution representation of some example patterns for signal structure extraction. Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the complete time series. The method is also robust against offsets and stable for signal to noise ratios larger than one. Its reliability was tested on 60 Holter ECG recordings of patients at the Department of Cardiology (University of Bonn). Due to the convincing results and its fast implementation the method can easily be used in clinical medicine. In particular, it solves the problem of automatic P wave detection in Holter ECG recordings.

  11. Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

    NASA Astrophysics Data System (ADS)

    Millán, María S.

    2012-10-01

    On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions.

  12. Heritable variation in colour patterns mediating individual recognition

    PubMed Central

    Choo, Juanita

    2017-01-01

    Understanding the developmental and evolutionary processes that generate and maintain variation in natural populations remains a major challenge for modern biology. Populations of Polistes fuscatus paper wasps have highly variable colour patterns that mediate individual recognition. Previous experimental and comparative studies have provided evidence that colour pattern diversity is the result of selection for individuals to advertise their identity. Distinctive identity-signalling phenotypes facilitate recognition, which reduces aggression between familiar individuals in P. fuscatus wasps. Selection for identity signals may increase phenotypic diversity via two distinct modes of selection that have different effects on genetic diversity. Directional selection for increased plasticity would greatly increase phenotypic diversity but decrease genetic diversity at associated loci. Alternatively, heritable identity signals under balancing selection would maintain genetic diversity at associated loci. Here, we assess whether there is heritable variation underlying colour pattern diversity used for facial recognition in a wild population of P. fuscatus wasps. We find that colour patterns are heritable and not Mendelian, suggesting that multiple loci are involved. Additionally, patterns of genetic correlations among traits indicated that many of the loci underlying colour pattern variation are unlinked and independently segregating. Our results support a model where the benefits of being recognizable maintain genetic variation at multiple unlinked loci that code for phenotypic diversity used for recognition. PMID:28386452

  13. 2D DOST based local phase pattern for face recognition

    NASA Astrophysics Data System (ADS)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2017-05-01

    A new two dimensional (2-D) Discrete Orthogonal Stcokwell Transform (DOST) based Local Phase Pattern (LPP) technique has been proposed for efficient face recognition. The proposed technique uses 2-D DOST as preliminary preprocessing and local phase pattern to form robust feature signature which can effectively accommodate various 3D facial distortions and illumination variations. The S-transform, is an extension of the ideas of the continuous wavelet transform (CWT), is also known for its local spectral phase properties in time-frequency representation (TFR). It provides a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information while maintaining a direct relationship with the Fourier spectrum which is unique in TFR. After utilizing 2-D Stransform as the preprocessing and build local phase pattern from extracted phase information yield fast and efficient technique for face recognition. The proposed technique shows better correlation discrimination compared to alternate pattern recognition techniques such as wavelet or Gabor based face recognition. The performance of the proposed method has been tested using the Yale and extended Yale facial database under different environments such as illumination variation and 3D changes in facial expressions. Test results show that the proposed technique yields better performance compared to alternate time-frequency representation (TFR) based face recognition techniques.

  14. Three-dimensional visualization for evaluating automated, geomorphic pattern-recognition analyses of crustal structures

    NASA Astrophysics Data System (ADS)

    Foley, M. G.

    1991-02-01

    We are developing and applying a suite of automated remote geologic analysis (RGA) methods at Pacific Northwest Laboratory (PNL) for extracting structural and tectonic patterns from digital models of topography and other spatially registered geophysical data. In analyzing a map area, the geologist employs a variety of spatial representations (e.g., topographic maps; oblique, vertical and vertical stereographic aerial photographs; satellite-sensor images) in addition to actual field observations to provide a basis for recognizing features (patterns) diagnostic or suggestive of various geologic and geomorphic features. We intend that our automated analyses of digital models of elevation use the same photogeologic pattern-recognition methods as the geologist's; otherwise there is no direct basis for manually evaluating results of the automated analysis. Any system for automating geologic analysis should extend the geologist's pattern-recognition abilities and quantify them, rather than replace them. This requirement means that results of automated structural pattern-recognition analyses must be evaluated by geologists using the same method that would be employed in manual field checking: visual examination of the three-dimensional relationships among rocks, erosional patterns, and identifiable structures. Interactive computer-graphics in quantitative (i.e., spatially registered), simulated three-dimensional perspective and stereo are thus critical to the integration and interpretation of topography, imagery, point data, RGA-identified fracture/fault planes, stratigraphy, contoured geophysical data, nonplanar surfaces, boreholes, and three-dimensional zones (e.g., crush zones at fracture intersections). This graphical interaction presents the megabytes of digital geologic and geophysical data to the geologist in the same spatial format that field observations would take, permitting direct evaluation of RGA methods and results.

  15. Three-dimensional visualization for evaluating automated, geomorphic pattern-recognition analyses of crustal structures

    SciTech Connect

    Foley, M.G.

    1991-02-01

    We are developing and applying a suite of automated remote geologic analysis (RGA) methods at Pacific Northwest Laboratory (PNL) for extracting structural and tectonic patterns from digital models of topography and other spatially registered geophysical data. In analyzing a map area, the geologist employs a variety of spatial representations (e.g., topographic maps; oblique, vertical and vertical stereographic aerial photographs; satellite-sensor images) in addition to actual field observations to provide a basis for recognizing features (patterns) diagnostic or suggestive of various geologic and geomorphic features. We intend that our automated analyses of digital models of elevation use the same photogeologic pattern-recognition methods as the geologist's; otherwise there is no direct basis for manually evaluating results of the automated analysis. Any system for automating geologic analysis should extend the geologist's pattern-recognition abilities and quantify them, rather than replace them. This requirement means that results of automated structural pattern-recognition analyses must be evaluated by geologists using the same method that would be employed in manual field checking: visual examination of the three-dimensional relationships among rocks, erosional patterns, and identifiable structures. Interactive computer-graphics in quantitative (i.e., spatially registered), simulated three-dimensional perspective and stereo are thus critical to the integration and interpretation of topography, imagery, point data, RGA-identified fracture/fault planes, stratigraphy, contoured geophysical data, nonplanar surfaces, boreholes, and three-dimensional zones (e.g., crush zones at fracture intersections). This graphical interaction presents the megabytes of digital geologic and geophysical data to the geologist in the same spatial format that field observations would take, permitting direct evaluation of RGA methods and results. 5 refs., 2 figs.

  16. Optical pattern recognition and Al algorithms and architectures for automatic target recognition (ATR) and computer vision

    NASA Astrophysics Data System (ADS)

    Casasent, David

    1987-04-01

    Optical Pattern Recognition has provided many attractive algorithms and architecture for advanced use in Automatic Target Recognition (ATR) and computer vision. This work is reviewed and highlighted in this paper. Attractive aspects of all of this research are: its attention to distortion-invariant, multi-target object recognition and the extensive testing which has been performed of these various architectures on large databases, as well as the design and fabrication of several quite compact optical processing architectures. Recent Artificial Intelligence (AI) techniques promise to further advance optical processing. These issues are summarized herein.

  17. The role of pattern recognition receptors in the innate recognition of Candida albicans

    PubMed Central

    Zheng, Nan-Xin; Wang, Yan; Hu, Dan-Dan; Yan, Lan; Jiang, Yuan-Ying

    2015-01-01

    Candida albicans is both a commensal microorganism in healthy individuals and a major fungal pathogen causing high mortality in immunocompromised patients. Yeast-hypha morphological transition is a well known virulence trait of C. albicans. Host innate immunity to C. albicans critically requires pattern recognition receptors (PRRs). In this review, we summarize the PRRs involved in the recognition of C. albicans in epithelial cells, endothelial cells, and phagocytic cells separately. We figure out the differential recognition of yeasts and hyphae, the findings on PRR-deficient mice, and the discoveries on human PRR-related single nucleotide polymorphisms (SNPs). PMID:25714264

  18. Processing Waveforms as Trees for Pattern Recognition.

    DTIC Science & Technology

    1986-05-01

    patterns (after Ganong (15]) 5.7 ECG Classification As in the previous example, waveforms were simulated with additive colored gaussian noise. In order to...Principles and Techniques- (AAPG Course Note Series 13), Amer. Assoc. Pet. Geol., Tulsa, OK,p. 86, (1984). [15] W. F. Ganong , Review of Medical Physiology. Lange, Los Altos, CA. pp. 393-408, (1973). /

  19. Learned pattern recognition using synthetic-discriminant-functions

    NASA Technical Reports Server (NTRS)

    Jared, David A.; Ennis, David J.

    1986-01-01

    A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed - binary tree and multilinked graph - along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.

  20. Ultrasonography of ovarian masses using a pattern recognition approach

    PubMed Central

    Jung, Sung Il

    2015-01-01

    As a primary imaging modality, ultrasonography (US) can provide diagnostic information for evaluating ovarian masses. Using a pattern recognition approach through gray-scale transvaginal US, ovarian masses can be diagnosed with high specificity and sensitivity. Doppler US may allow ovarian masses to be diagnosed as benign or malignant with even greater confidence. In order to differentiate benign and malignant ovarian masses, it is necessary to categorize ovarian masses into unilocular cyst, unilocular solid cyst, multilocular cyst, multilocular solid cyst, and solid tumor, and then to detect typical US features that demonstrate malignancy based on pattern recognition approach. PMID:25797108

  1. Learned pattern recognition using synthetic-discriminant-functions

    NASA Technical Reports Server (NTRS)

    Jared, David A.; Ennis, David J.

    1986-01-01

    A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed - binary tree and multilinked graph - along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.

  2. Ultrasonography of ovarian masses using a pattern recognition approach.

    PubMed

    Jung, Sung Il

    2015-07-01

    As a primary imaging modality, ultrasonography (US) can provide diagnostic information for evaluating ovarian masses. Using a pattern recognition approach through gray-scale transvaginal US, ovarian masses can be diagnosed with high specificity and sensitivity. Doppler US may allow ovarian masses to be diagnosed as benign or malignant with even greater confidence. In order to differentiate benign and malignant ovarian masses, it is necessary to categorize ovarian masses into unilocular cyst, unilocular solid cyst, multilocular cyst, multilocular solid cyst, and solid tumor, and then to detect typical US features that demonstrate malignancy based on pattern recognition approach.

  3. Detection and recognition of analytes based on their crystallization patterns

    DOEpatents

    Morozov, Victor; Bailey, Charles L.; Vsevolodov, Nikolai N.; Elliott, Adam

    2008-05-06

    The invention contemplates a method for recognition of proteins and other biological molecules by imaging morphology, size and distribution of crystalline and amorphous dry residues in droplets (further referred to as "crystallization pattern") containing predetermined amount of certain crystal-forming organic compounds (reporters) to which protein to be analyzed is added. It has been shown that changes in the crystallization patterns of a number of amino-acids can be used as a "signature" of a protein added. It was also found that both the character of changer in the crystallization patter and the fact of such changes can be used as recognition elements in analysis of protein molecules.

  4. Comparative wavelet, PLP, and LPC speech recognition techniques on the Hindi speech digits database

    NASA Astrophysics Data System (ADS)

    Mishra, A. N.; Shrotriya, M. C.; Sharan, S. N.

    2010-02-01

    In view of the growing use of automatic speech recognition in the modern society, we study various alternative representations of the speech signal that have the potential to contribute to the improvement of the recognition performance. In this paper wavelet based features using different wavelets are used for Hindi digits recognition. The recognition performance of these features has been compared with Linear Prediction Coefficients (LPC) and Perceptual Linear Prediction (PLP) features. All features have been tested using Hidden Markov Model (HMM) based classifier for speaker independent Hindi digits recognition. The recognition performance of PLP features is11.3% better than LPC features. The recognition performance with db10 features has shown a further improvement of 12.55% over PLP features. The recognition performance with db10 is best among all wavelet based features.

  5. Pattern Recognition of Adsorbing HP Lattice Proteins

    NASA Astrophysics Data System (ADS)

    Wilson, Matthew S.; Shi, Guangjie; Wüst, Thomas; Landau, David P.; Schmid, Friederike

    2015-03-01

    Protein adsorption is relevant in fields ranging from medicine to industry, and the qualitative behavior exhibited by course-grained models could shed insight for further research in such fields. Our study on the selective adsorption of lattice proteins utilizes the Wang-Landau algorithm to simulate the Hydrophobic-Polar (H-P) model with an efficient set of Monte Carlo moves. Each substrate is modeled as a square pattern of 9 lattice sites which attract either H or P monomers, and are located on an otherwise neutral surface. The fully enumerated set of 102 unique surfaces is simulated with each protein sequence. A collection of 27-monomer sequences is used- each of which is non-degenerate and protein-like. Thermodynamic quantities such as the specific heat and free energy are calculated from the density of states, and are used to investigate the adsorption of lattice proteins on patterned substrates. Research supported by NSF.

  6. Pattern recognition for electroencephalographic signals based on continuous neural networks.

    PubMed

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2016-07-01

    This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results.

  7. Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

    NASA Technical Reports Server (NTRS)

    Heydorn, R. D.

    1984-01-01

    The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth.

  8. Auditory orientation in crickets: Pattern recognition controls reactive steering

    NASA Astrophysics Data System (ADS)

    Poulet, James F. A.; Hedwig, Berthold

    2005-10-01

    Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

  9. Accurate invariant pattern recognition for perspective camera model

    NASA Astrophysics Data System (ADS)

    Serikova, Mariya G.; Pantyushina, Ekaterina N.; Zyuzin, Vadim V.; Korotaev, Valery V.; Rodrigues, Joel J. P. C.

    2015-05-01

    In this work we present a pattern recognition method based on geometry analysis of a flat pattern. The method provides reliable detection of the pattern in the case when significant perspective deformation is present in the image. The method is based on the fact that collinearity of the lines remains unchanged under perspective transformation. So the recognition feature is the presence of two lines, containing four points each. Eight points form two squares for convenience of applying corner detection algorithms. The method is suitable for automatic pattern detection in a dense environment of false objects. In this work we test the proposed method for statistics of detection and algorithm's performance. For estimation of pattern detection quality we performed image simulation process with random size and spatial frequency of background clutter while both translational (range varied from 200 mm to 1500 mm) and rotational (up to 60°) deformations in given pattern position were added. Simulated measuring system included a camera (4000x4000 sensor with 25 mm lens) and a flat pattern. Tests showed that the proposed method demonstrates no more than 1% recognition error when number of false targets is up to 40.

  10. Analog parallel processor hardware for high speed pattern recognition

    NASA Technical Reports Server (NTRS)

    Daud, T.; Tawel, R.; Langenbacher, H.; Eberhardt, S. P.; Thakoor, A. P.

    1990-01-01

    A VLSI-based analog processor for fully parallel, associative, high-speed pattern matching is reported. The processor consists of two main components: an analog memory matrix for storage of a library of patterns, and a winner-take-all (WTA) circuit for selection of the stored pattern that best matches an input pattern. An inner product is generated between the input vector and each of the stored memories. The resulting values are applied to a WTA network for determination of the closest match. Patterns with up to 22 percent overlap are successfully classified with a WTA settling time of less than 10 microsec. Applications such as star pattern recognition and mineral classification with bounded overlap patterns have been successfully demonstrated. This architecture has a potential for an overall pattern matching speed in excess of 10 exp 9 bits per second for a large memory.

  11. Analog parallel processor hardware for high speed pattern recognition

    NASA Technical Reports Server (NTRS)

    Daud, T.; Tawel, R.; Langenbacher, H.; Eberhardt, S. P.; Thakoor, A. P.

    1990-01-01

    A VLSI-based analog processor for fully parallel, associative, high-speed pattern matching is reported. The processor consists of two main components: an analog memory matrix for storage of a library of patterns, and a winner-take-all (WTA) circuit for selection of the stored pattern that best matches an input pattern. An inner product is generated between the input vector and each of the stored memories. The resulting values are applied to a WTA network for determination of the closest match. Patterns with up to 22 percent overlap are successfully classified with a WTA settling time of less than 10 microsec. Applications such as star pattern recognition and mineral classification with bounded overlap patterns have been successfully demonstrated. This architecture has a potential for an overall pattern matching speed in excess of 10 exp 9 bits per second for a large memory.

  12. Distortion-tolerant 3-D object recognition by using single exposure on-axis digital holography.

    PubMed

    Kim, Daesuk; Javidi, Bahram

    2004-11-01

    We present a distortion-tolerant 3-D object recognition system using single exposure on-axis digital holography. In contrast to distortion-tolerant 3-D object recognition employing conventional phase shifting scheme which requires multiple exposures, our proposed method requires only one single digital hologram to be synthesized and used for distortion-tolerant 3-D object recognition. A benefit of the single exposure based on-axis approach is enhanced practicality of digital holography for distortion-tolerant 3-D object recognition in terms of its simplicity and high tolerance to external scene parameters such as moving targets. This paper shows experimentally, that single exposure on-axis digital holography is capable of providing a distortion-tolerant 3-D object recognition capability.

  13. A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

    PubMed

    Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

    2015-11-01

    This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

  14. Photonic implementation of Hopfield neural network for associative pattern recognition

    NASA Astrophysics Data System (ADS)

    Munshi, Soumika; Bhattacharyya, Siddhartha; Datta, Asit K.

    2001-09-01

    An optical matrix-vector multiplier has ben efficiently used for photonic implementation of Hopfield network model, which is used for binary pattern recognition. Training matrices are recorded on electrically addressed spatial light modulator, where each matrix is composed of the same row of each pattern, that the network is being trained with. After training, if an unknown pattern is presented to the network in the form of a vector, the output vector is obtained by the element that has the highest magnitude through a winner- take-all algorithm. Pattern can be recognized even if the input is noisy and distorted.

  15. Large-memory real-time multichannel multiplexed pattern recognition

    NASA Astrophysics Data System (ADS)

    Gregory, D. A.; Liu, H. K.

    1984-12-01

    The principle and experimental design of a real-time multichannel multiplexed optical pattern recognition system via use of a 25-focus dichromated gelatin holographic lens (hololens) are described. Each of the 25 foci of the hololens may have a storage and matched filtering capability approaching that of a single-lens correlator. If the space-bandwidth product of an input image is limited, as is true in most practical cases, the 25-focus hololens system has 25 times the capability of a single lens. Experimental results have shown that the interfilter noise is not serious. The system has already demonstrated the storage and recognition of over 70 matched filters - which is a larger capacity than any optical pattern recognition system reported to date.

  16. Large-memory real-time multichannel multiplexed pattern recognition

    NASA Technical Reports Server (NTRS)

    Gregory, D. A.; Liu, H. K.

    1984-01-01

    The principle and experimental design of a real-time multichannel multiplexed optical pattern recognition system via use of a 25-focus dichromated gelatin holographic lens (hololens) are described. Each of the 25 foci of the hololens may have a storage and matched filtering capability approaching that of a single-lens correlator. If the space-bandwidth product of an input image is limited, as is true in most practical cases, the 25-focus hololens system has 25 times the capability of a single lens. Experimental results have shown that the interfilter noise is not serious. The system has already demonstrated the storage and recognition of over 70 matched filters - which is a larger capacity than any optical pattern recognition system reported to date.

  17. Large-memory real-time multichannel multiplexed pattern recognition

    NASA Technical Reports Server (NTRS)

    Gregory, D. A.; Liu, H. K.

    1984-01-01

    The principle and experimental design of a real-time multichannel multiplexed optical pattern recognition system via use of a 25-focus dichromated gelatin holographic lens (hololens) are described. Each of the 25 foci of the hololens may have a storage and matched filtering capability approaching that of a single-lens correlator. If the space-bandwidth product of an input image is limited, as is true in most practical cases, the 25-focus hololens system has 25 times the capability of a single lens. Experimental results have shown that the interfilter noise is not serious. The system has already demonstrated the storage and recognition of over 70 matched filters - which is a larger capacity than any optical pattern recognition system reported to date.

  18. Biometric verification based on grip-pattern recognition

    NASA Astrophysics Data System (ADS)

    Veldhuis, Raymond N.; Bazen, Asker M.; Kauffman, Joost A.; Hartel, Pieter

    2004-06-01

    This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 × 44 piezoresistive elements is used to measure the grip pattern. An interface has been developed to acquire pressure images from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification algorithm, which is currently implemented in software on a PC. The verification algorithm is based on a likelihoodratio classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern recognition for biometric verification.

  19. Patterns of muscle activity for digital coarticulation

    PubMed Central

    Winges, Sara A.; Furuya, Shinichi; Faber, Nathaniel J.

    2013-01-01

    Although piano playing is a highly skilled task, basic features of motor pattern generation may be shared across tasks involving fine movements, such as handling coins, fingering food, or using a touch screen. The scripted and sequential nature of piano playing offered the opportunity to quantify the neuromuscular basis of coarticulation, i.e., the manner in which the muscle activation for one sequential element is altered to facilitate production of the preceding and subsequent elements. Ten pianists were asked to play selected pieces with the right hand at a uniform tempo. Key-press times were recorded along with the electromyographic (EMG) activity from seven channels: thumb flexor and abductor muscles, a flexor for each finger, and the four-finger extensor muscle. For the thumb and index finger, principal components of EMG waveforms revealed highly consistent variations in the shape of the flexor bursts, depending on the type of sequence in which a particular central key press was embedded. For all digits, the duration of the central EMG burst scaled, along with slight variations across subjects in the duration of the interkeystroke intervals. Even within a narrow time frame (about 100 ms) centered on the central EMG burst, the exact balance of EMG amplitudes across multiple muscles depended on the nature of the preceding and subsequent key presses. This fails to support the idea of fixed burst patterns executed in sequential phases and instead provides evidence for neuromuscular coarticulation throughout the time course of a hand movement sequence. PMID:23596338

  20. Human pattern recognition: parallel processing and perceptual learning.

    PubMed

    Fahle, M

    1994-01-01

    A new theory of visual object recognition by Poggio et al that is based on multidimensional interpolation between stored templates requires fast, stimulus-specific learning in the visual cortex. Indeed, performance in a number of perceptual tasks improves as a result of practice. We distinguish between two phases of learning a vernier-acuity task, a fast one that takes place within less than 20 min and a slow phase that continues over 10 h of training and probably beyond. The improvement is specific for relatively 'simple' features, such as the orientation of the stimulus presented during training, for the position in the visual field, and for the eye through which learning occurred. Some of these results are simulated by means of a computer model that relies on object recognition by multidimensional interpolation between stored templates. Orientation specificity of learning is also found in a jump-displacement task. In a manner parallel to the improvement in performance, cortical potentials evoked by the jump displacement tend to decrease in latency and to increase in amplitude as a result of training. The distribution of potentials over the brain changes significantly as a result of repeated exposure to the same stimulus. The results both of psychophysical and of electrophysiological experiments indicate that some form of perceptual learning might occur very early during cortical information processing. The hypothesis that vernier breaks are detected 'early' during pattern recognition is supported by the fact that reaction times for the detection of verniers depend hardly at all on the number of stimuli presented simultaneously. Hence, vernier breaks can be detected in parallel at different locations in the visual field, indicating that deviation from straightness is an elementary feature for visual pattern recognition in humans that is detected at an early stage of pattern recognition. Several results obtained during the last few years are reviewed, some new

  1. Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Amador, Jose J (Inventor)

    2007-01-01

    A method of mid-level pattern recognition provides for a pose invariant Hough Transform by parametrizing pairs of points in a pattern with respect to at least two reference points, thereby providing a parameter table that is scale- or rotation-invariant. A corresponding inverse transform may be applied to test hypothesized matches in an image and a distance transform utilized to quantify the level of match.

  2. Self-amplified optical pattern recognition system

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor)

    1994-01-01

    A self amplifying optical pattern recognizer includes a geometric system configuration similar to that of a Vander Lugt holographic matched filter configuration with a photorefractive crystal specifically oriented with respect to the input beams. An extraordinarily polarized, spherically converging object image beam is formed by laser illumination of an input object image and applied through a photorefractive crystal, such as a barium titanite (BaTiO.sub.3) crystal. A volume or thin-film dif ORIGIN OF THE INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) in which the Contractor has elected to retain title.

  3. Pattern Recognition Receptors in Innate Immunity, Host Defense, and Immunopathology

    ERIC Educational Resources Information Center

    Suresh, Rahul; Mosser, David M.

    2013-01-01

    Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue.…

  4. [Chemical pattern recognition of traditional Chinese medicine kudingcha (I)].

    PubMed

    Su, W; Wu, Z; Chen, J; He, X; Li, J

    1998-03-01

    In this paper, the non-linear mapping method of pattern recognition was adopted to classify 78 samples of traditional Chinese medicine Kudingcha, with macro and trace elements as classified characteristic features. Ilex cornuta Lindl., Ilex latifolia Thunb. and Ligustrum lucidum Ait. were identified accurately. The results agree with those from pharmacognosy. This paper provides a new method for identification of traditional Chinese medicine.

  5. Pattern Recognition Receptors in Innate Immunity, Host Defense, and Immunopathology

    ERIC Educational Resources Information Center

    Suresh, Rahul; Mosser, David M.

    2013-01-01

    Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue.…

  6. Linear Invariant Multiclass Component Spaces For Optical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Hester, Charles F.

    1983-04-01

    Optical processing systems which perform linear transformations on image data at high rates are ideal for image pattern recognition systems. As a result of this processing capability, the linear opera-tion of matched spatial filtering has been explored extensively for pattern recognition. For many practical pattern recognition problems, however, multiclass filtering must be used to overcome the variations of input objects due to image scale changes, image rotations, object aspect differences and sensor differences. Hester and Casasent have shown that a linear mapping can be constructed which images all the class elements of a multiclass set into one out-put element or value. This special multi-class filter concept is extended in this paper to show that a subspace of the multi-class set exists that is invariant with respect to the multiclass mapping under linear operations. The concept of this in-variant space and its generation is detailed and a single example given. A typical optical processing architecture using these invariant elements as filters in an associative pattern recognition system is also presented.

  7. [Chemical pattern recognition of traditional Chinese medicine kudingcha (II)].

    PubMed

    Su, W; Wu, Z; He, X; Chen, J

    1998-04-01

    In this paper, the HPLC data from 78 samples of Kudingcha were treated with back propagation algorithm of artifical neural network pattern recognition, and the computer-aided classification of Ilex cornuta Lindl., Ilex latifolia Thunb. and Ligustrum lucidum Ait. was accomplished. This paper provides a scientific, advanced and feasible method for identification of traditional Chinese medicine.

  8. Optical pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor)

    1989-01-01

    Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.

  9. Optical pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor)

    1989-01-01

    Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.

  10. Optical pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

    NASA Astrophysics Data System (ADS)

    Liu, Hua-Kuang

    Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.

  11. ANN-TREE: a hybrid method for pattern recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Lijia; Franklin, Stan

    1993-09-01

    Here we present a hybrid method of generating a hierarchical recognition system based on example learning. The method is 'hybrid' in that it uses both conventional Artificial Intelligence and Artificial Neural Network techniques. The integrated hierarchical recognition system, called IHKB (integrated hierarchical knowledge base), has a tree structure consisting of nodes and leaves. Each node is indexed by an attribute set and contains a small Kohonen network (KN). Each leaf represents a recognition class. The system uses a conceptual function to instruct the process of attribute choosing. Whenever a suitable attribute set is obtained for a certain group of training examples, a small Kohonen net is built and trained with those examples. This allows the machine to focus on special features of these training examples and thus to better describe the special characteristics of these patterns. Typically, there are many KNs in a IHKB, the number depending on the number of attribute sets. The position of each KN in the tree is fixed automatically. When the construction is complete, the training examples are classified by Kohonen nets, and recognition is achieved by a path from the root of the tree to a leaf. The method has been tested on individual handwritten character recognition, showing that high recognition rates can be achieved given enough training examples.

  12. 3D face database for human pattern recognition

    NASA Astrophysics Data System (ADS)

    Song, LiMei; Lu, Lu

    2008-10-01

    Face recognition is an essential work to ensure human safety. It is also an important task in biomedical engineering. 2D image is not enough for precision face recognition. 3D face data includes more exact information, such as the precision size of eyes, mouth, etc. 3D face database is an important part in human pattern recognition. There is a lot of method to get 3D data, such as 3D laser scan system, 3D phase measurement, shape from shading, shape from motion, etc. This paper will introduce a non-orbit, non-contact, non-laser 3D measurement system. The main idea is from shape from stereo technique. Two cameras are used in different angle. A sequence of light will project on the face. Human face, human head, human tooth, human body can all be measured by the system. The visualization data of each person can form to a large 3D face database, which can be used in human recognition. The 3D data can provide a vivid copy of a face, so the recognition exactness can be reached to 100%. Although the 3D data is larger than 2D image, it can be used in the occasion where only few people include, such as the recognition of a family, a small company, etc.

  13. A new concept of vertically integrated pattern recognition associative memory

    SciTech Connect

    Liu, Ted; Hoff, Jim; Deptuch, Grzegorz; Yarema, Ray; /Fermilab

    2011-11-01

    Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R and D proposal based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R and D project will be reported in the future. Here we will only focus on the concept of this new approach.

  14. Quantum Mechanics, Pattern Recognition, and the Mammalian Brain

    NASA Astrophysics Data System (ADS)

    Chapline, George

    2008-10-01

    Although the usual way of representing Markov processes is time asymmetric, there is a way of describing Markov processes, due to Schrodinger, which is time symmetric. This observation provides a link between quantum mechanics and the layered Bayesian networks that are often used in automated pattern recognition systems. In particular, there is a striking formal similarity between quantum mechanics and a particular type of Bayesian network, the Helmholtz machine, which provides a plausible model for how the mammalian brain recognizes important environmental situations. One interesting aspect of this relationship is that the "wake-sleep" algorithm for training a Helmholtz machine is very similar to the problem of finding the potential for the multi-channel Schrodinger equation. As a practical application of this insight it may be possible to use inverse scattering techniques to study the relationship between human brain wave patterns, pattern recognition, and learning. We also comment on whether there is a relationship between quantum measurements and consciousness.

  15. Automatic Mexican sign language and digits recognition using normalized central moments

    NASA Astrophysics Data System (ADS)

    Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

    2016-09-01

    This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

  16. Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

    NASA Astrophysics Data System (ADS)

    Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.

    2013-02-01

    The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

  17. Sensitivity and specificity of a digit symbol recognition trial in the identification of response bias.

    PubMed

    Kim, Nancy; Boone, Kyle B; Victor, Tara; Lu, Po; Keatinge, Carolyn; Mitchell, Cary

    2010-08-01

    Recently published practice standards recommend that multiple effort indicators be interspersed throughout neuropsychological evaluations to assess for response bias, which is most efficiently accomplished through use of effort indicators from standard cognitive tests already included in test batteries. The present study examined the utility of a timed recognition trial added to standard administration of the WAIS-III Digit Symbol subtest in a large sample of "real world" noncredible patients (n=82) as compared with credible neuropsychology clinic patients (n=89). Scores from the recognition trial were more sensitive in identifying poor effort than were standard Digit Symbol scores, and use of an equation incorporating Digit Symbol Age-Corrected Scaled Scores plus accuracy and time scores from the recognition trial was associated with nearly 80% sensitivity at 88.7% specificity. Thus, inclusion of a brief recognition trial to Digit Symbol administration has the potential to provide accurate assessment of response bias.

  18. Natural cytotoxicity receptors: pattern recognition and involvement of carbohydrates.

    PubMed

    Porgador, Angel

    2005-02-23

    Natural cytotoxicity receptors (NCRs), expressed by natural killer (NK) cells, trigger NK lysis of tumor and virus-infected cells on interaction with cell-surface ligands of these target cells. We have determined that viral hemagglutinins expressed on the surface of virus-infected cells are involved in the recognition by the NCRs, NKp44 and NKp46. Recognition of tumor cells by the NCRs NKp30 and NKp46 involves heparan sulfate epitopes expressed on the tumor cell membrane. Our studies provide new evidence for the identity of the ligands for NCRs and indicate that a broader definition should be applied to pathological patterns recognized by innate immune receptors. Since nonmicrobial endogenous carbohydrate structures contribute significantly to this recognition, there is an imperative need to develop appropriate tools for the facile sequencing of carbohydrate moieties.

  19. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-ling; Su, Qi-ya; Sun, Qun; Yang, Li-ming

    2012-05-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling since it has advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds was proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA, PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results of three different spectral regions show that the performances of three methods: PCA+SVM, LLE+SVM, PLS+SVM are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.

  20. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-Ling; Su, Qi-Ya; Sun, Qun; Yang, Li-Ming

    2012-06-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling due to its advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds is proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA and PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results for three different spectral regions show that the performances of three methods, i. e. PCA+SVM, LLE+SVM, PLS+SVM, are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.

  1. Pattern recognition software and techniques for biological image analysis.

    PubMed

    Shamir, Lior; Delaney, John D; Orlov, Nikita; Eckley, D Mark; Goldberg, Ilya G

    2010-11-24

    The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

  2. Pattern Recognition Software and Techniques for Biological Image Analysis

    PubMed Central

    Shamir, Lior; Delaney, John D.; Orlov, Nikita; Eckley, D. Mark; Goldberg, Ilya G.

    2010-01-01

    The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays. PMID:21124870

  3. Rotation invariant pattern recognition with a volume holographic wavelet correlation processor

    NASA Astrophysics Data System (ADS)

    Tan, Wenzhao; Xue, Qingzeng; Yan, Yingbai; Jin, Guofan

    2003-02-01

    A volume holographic wavelet correlation processor for performing rotation invariant pattern recognition is suggested. It uses wavelet transform to get the digital edge extraction of the original object and a single circular harmonic component is used as the matched filter to get good rotation invariance. The new filter used in this method is called wavelet circular harmonic component filter (WCHCF). Simulation results validate the theory and the experiment to recognize human faces with any rotation angle shows the utility of the newly proposed method.

  4. Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

    NASA Astrophysics Data System (ADS)

    Kamada, Kohji; Enokido, Uhji; Ogawa, Seiji

    1999-05-01

    A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak 252Cf n-γ source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system.

  5. Markov sequential pattern recognition : dependency and the unknown class.

    SciTech Connect

    Malone, Kevin Thomas; Haschke, Greg Benjamin; Koch, Mark William

    2004-10-01

    The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.

  6. Real-Time Pattern Recognition - An Industrial Example

    NASA Astrophysics Data System (ADS)

    Fitton, Gary M.

    1981-11-01

    Rapid advancements in cost effective sensors and micro computers are now making practical the on-line implementation of pattern recognition based systems for a variety of industrial applications requiring high processing speeds. One major application area for real time pattern recognition is in the sorting of packaged/cartoned goods at high speed for automated warehousing and return goods cataloging. While there are many OCR and bar code readers available to perform these functions, it is often impractical to use such codes (package too small, adverse esthetics, poor print quality) and an approach which recognizes an item by its graphic content alone is desirable. This paper describes a specific application within the tobacco industry, that of sorting returned cigarette goods by brand and size.

  7. Learning pattern recognition and decision making in the insect brain

    NASA Astrophysics Data System (ADS)

    Huerta, R.

    2013-01-01

    We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

  8. Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Andriana, A. S.; Prihatmanto, D.; Hidaya, E. M. I.; Supriana, I.; Machbub, C.

    2017-01-01

    Understanding images by recognizing its objects is still a challenging task. Face elements detection has been developed by researchers but not yet shows enough information (low resolution in information) needed for recognizing objects. Available face recognition methods still have error in classification and need a huge amount of examples which may still be incomplete. Another approach which is still rare in understanding images uses pattern structures or syntactic grammars describing shape detail features. Image pixel values are also processed as signal patterns which are approximated by mathematical function curve fitting. This paper attempts to add contiguous uniform deviation method to curve fitting algorithm to increase applicability in image recognition system related to object movement. The combination of multiple linear regression and contiguous uniform deviation method are applied to the function of image pixel values, and show results in higher resolution (more information) of visual object detail description in object movement.

  9. Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

    PubMed Central

    Kim, Eungyeong; Lee, Seok; Kim, Jae Hun; Kim, Chulki; Byun, Young Tae; Kim, Hyung Seok; Lee, Taikjin

    2012-01-01

    This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals. PMID:23443378

  10. Pattern recognition for Space Applications Center director's discretionary fund

    NASA Technical Reports Server (NTRS)

    Singley, M. E.

    1984-01-01

    Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

  11. System integration of pattern recognition, adaptive aided, upper limb prostheses

    NASA Technical Reports Server (NTRS)

    Lyman, J.; Freedy, A.; Solomonow, M.

    1975-01-01

    The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

  12. Neurocomputing methods for pattern recognition in nuclear physics

    SciTech Connect

    Gyulassy, M.; Dong, D.; Harlander, M.

    1991-12-31

    We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

  13. Online pattern recognition for the ALICE high level trigger

    NASA Astrophysics Data System (ADS)

    Bramm, R.; Helstrup, H.; Lien, J.; Lindenstruth, V.; Loizides, C.; Rohrich, D.; Skaali, B.; Steinbeck, T.; Stock, R.; Ullaland, K.; Vestbø, A.; Wiebalck, A.; Alice Collaboration

    2003-04-01

    The ALICE High Level Trigger system needs to reconstruct events online at high data rates. Focusing on the Time Projection Chamber we present two pattern recognition methods under investigation: the sequential approach (cluster finding, track follower) and the iterative approach (Hough Transform, cluster assignment, re-fitting). The implementation of the former in hardware indicates that we can reach the designed inspection rate for p-p collisions of 1 kHz with 98% efficiency.

  14. Linear Programming and Its Application to Pattern Recognition Problems

    NASA Technical Reports Server (NTRS)

    Omalley, M. J.

    1973-01-01

    Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

  15. A new paradigm for pattern recognition of drugs.

    PubMed

    Potemkin, Vladimir A; Grishina, Maria A

    2008-01-01

    A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D/3D quantitative structure-activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS/MC (multiconformational), is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for the detailed study of the selected conformers' electron density and for the search for the electron structure fragments that determine the pharmacophore and antipharmacophore parts of the compounds. In this work we suggest using a new AlteQ method for the evaluation of the molecular electron density. AlteQ describes the experimental electron density (determined by low-temperature highly accurate X-ray analysis) much better than a number of quantum approaches. Herein this is shown using a comparison of the computed electron density with the results of highly accurate X-ray analysis. In the present study the desirability function is used for the first time for the analysis of the effects of the electron structure in the process of pattern recognition of active and inactive compounds. The suggested method for pattern recognition has been used for the investigation of various sets of compounds such as DNA-antimetabolites, fXa inhibitors, 5-HT(1A), and alpha(1)-AR receptors inhibitors. The pharmacophore and antipharmacophore fragments have been found in the electron structures of the compounds. It has been shown that the pattern recognition cross-validation quality for the datasets is unity.

  16. Amplitude-modulated circular-harmonic filter for pattern recognition.

    PubMed

    Chen, X W; Chen, Z P

    1995-02-10

    An amplitude-modulated circular-harmonic filter is proposed for rotation-invariant pattern recognition. We investigate the filter characteristics by varying two design parameters, A(ρ) and B(ρ), and select optimum values to design an amplitude-modulated circular-harmonic filter. When compared with the phase-only circular-harmonic filter, the amplitude-modulated circular-harmonic filter is found to yield a sharper correlation peak, a better noise tolerance, and an improved correlation discrimination.

  17. Conditional random fields for pattern recognition applied to structured data

    DOE PAGES

    Burr, Tom; Skurikhin, Alexei

    2015-07-14

    In order to predict labels from an output domain, Y, pattern recognition is used to gather measurements from an input domain, X. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features betweenmore » parts of the model are often correlated. Thus, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. Our paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.« less

  18. Conditional random fields for pattern recognition applied to structured data

    SciTech Connect

    Burr, Tom; Skurikhin, Alexei

    2015-07-14

    Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

  19. Atmospheric propagation effects on pattern recognition by neural networks

    NASA Astrophysics Data System (ADS)

    Giever, John C.; Hoock, Donald W., Jr.

    1991-07-01

    Smart electro-optical systems of the future will need to be adaptive and robust to function in different environments. In 1989 the authors reported how atmospheric losses in contrast, resolution, edge detail, and signal to noise adversely affect image-based classification using linear matched filters and how the atmosphere alters features such as gray-level moments. They also showed that the performance changes with atmospheric path radiance and transmittance are predictable, however, and that some effects can be mitigated automatically by including the atmosphere as a separate training class. This paper extends that analysis to atmospheric effects on pattern recognition by neural network classifiers. The neural net pattern recognition methods considered here are single- and multi-layer perceptron networks trained with back-propagation. Image classifier performance under different atmospheric propagation conditions is shown to be easily predicted for simple single-layer neural nets. This leads to a specific training strategy to minimize the impact of propagation losses by including the atmosphere as a separate training class. This same strategy also improves the performance of multi-layer neural networks. Examples are given of classification of a vehicle partly obscured by highly scattering white smoke and highly absorptive black smoke. Other methods are being investigated that affect the performance and training convergence properties of neural net pattern recognition in atmospheres.

  20. Plant systems for recognition of pathogen-associated molecular patterns.

    PubMed

    Postel, Sandra; Kemmerling, Birgit

    2009-12-01

    Research of the last decade has revealed that plant immunity consists of different layers of defense that have evolved by the co-evolutional battle of plants with its pathogens. Particular light has been shed on PAMP- (pathogen-associated molecular pattern) triggered immunity (PTI) mediated by pattern recognition receptors. Striking similarities exist between the plant and animal innate immune system that point for a common optimized mechanism that has evolved independently in both kingdoms. Pattern recognition receptors (PRRs) from both kingdoms consist of leucine-rich repeat receptor complexes that allow recognition of invading pathogens at the cell surface. In plants, PRRs like FLS2 and EFR are controlled by a co-receptor SERK3/BAK1, also a leucine-rich repeat receptor that dimerizes with the PRRs to support their function. Pathogens can inject effector proteins into the plant cells to suppress the immune responses initiated after perception of PAMPs by PRRs via inhibition or degradation of the receptors. Plants have acquired the ability to recognize the presence of some of these effector proteins which leads to a quick and hypersensitive response to arrest and terminate pathogen growth.

  1. Spatial pattern recognition of seismic events in South West Colombia

    NASA Astrophysics Data System (ADS)

    Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

    2013-09-01

    Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

  2. Pattern Recognition Using The Ring-Wedge Detector And Neural-Network Software

    NASA Astrophysics Data System (ADS)

    George, Nicholas; Wang, Shen-Ge; Venable, Dennis L.

    1989-10-01

    In pattern recognition and in optical metrology, optical transform systems have been widely applied. Their use is particularly appropriate when the object is detailed and the recognition depends upon features that can be coarsely sampled in the transform space. Now with the advent of neural-network software, it is shown that the major difficulty in applying this optoelectronic hybrid is overcome. Using the ring-wedge photodetector and neural-network software, we illustrate the classification technique using thumbprints. This is a problem of known difficulty to us. Instead of a 4 person-month effort to devise software for its solution, we find that a 4-hour effort is all that is required. Other experiments also discussed are the sorting of photographs of cats and dogs, particulate suspensions, and image quality of digital halftones. All of these are shown to be promising examples for the application of the ring-wedge detector and neural-network software.

  3. [Recognition of commensal microflora by pattern recognition receptors in human physiology and pathology].

    PubMed

    Bondarenko, V M; Likhoded, V G

    2012-01-01

    Contemporary data on the interaction of commensal microflora and Toll-like pattern recognition receptors are presented. These receptors recognize normal intestine microflora in physiological conditions, and this interaction is necessary for the maintenance of homeostasis and damage reparation of the intestine, for the induction of heat shock cytoprotective proteins. As a side effect in disruption of immunologic tolerance and misbalance of protective immunological mechanisms, multiorgan pathologic changes of organs and tissues may develop, including chronic inflammation processes of various localization.

  4. Speaker-independent isolated-digit recognition using neural networks and hidden Markov model

    NASA Astrophysics Data System (ADS)

    McGuire, Michael; Ingraham, Diane; Cuperman, Vladimir

    A speaker independent isolated-digit recognition system is described which incorporates neural networks for vector quantization (VQ) and postprocessing of VQ and hidden Markov model (HMM) decision information. Vector quantization was performed using word-based parameter specific codebooks generated with a frequency sensitive competitive learning (FSCL) training algorithm. A single-layer perceptron neural network was used to increase recognition performance by postprocessing VQ distortion and HMM output probabilistic information. Experiments using both studio and telephone-line recorded databases demonstrate the FSCL VQ-HMM system without postprocessing achieves a recognition performance increase when compared to a previously developed VQ-HMM digit recognition system. With the addition of postprocessing to the FSCL VQ-HMM system, recognition performance was further increased.

  5. Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?

    PubMed

    Ashkenazi, Sarit; Mark-Zigdon, Nitza; Henik, Avishai

    2013-01-01

    The abilities of children diagnosed with developmental dyscalculia (DD) were examined in two types of object enumeration: subitizing, and small estimation (5-9 dots). Subitizing is usually defined as a fast and accurate assessment of a number of small dots (range 1 to 4 dots), and estimation is an imprecise process to assess a large number of items (range 5 dots or more). Based on reaction time (RT) and accuracy analysis, our results indicated a deficit in the subitizing and small estimation range among DD participants in relation to controls. There are indications that subitizing is based on pattern recognition, thus presenting dots in a canonical shape in the estimation range should result in a subitizing-like pattern. In line with this theory, our control group presented a subitizing-like pattern in the small estimation range for canonically arranged dots, whereas the DD participants presented a deficit in the estimation of canonically arranged dots. The present finding indicates that pattern recognition difficulties may play a significant role in both subitizing and subitizing deficits among those with DD. © 2012 Blackwell Publishing Ltd.

  6. Pattern recognition with “materials that compute”

    PubMed Central

    Fang, Yan; Yashin, Victor V.; Levitan, Steven P.; Balazs, Anna C.

    2016-01-01

    Driven by advances in materials and computer science, researchers are attempting to design systems where the computer and material are one and the same entity. Using theoretical and computational modeling, we design a hybrid material system that can autonomously transduce chemical, mechanical, and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemomechanical oscillations of the BZ gels deflect the PZ layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, where the mode of synchronization depends on the polarity of the PZ. We show that the network of coupled, synchronizing BZ-PZ oscillators can perform pattern recognition. The “stored” patterns are set of polarities of the individual BZ-PZ units, and the “input” patterns are coded through the initial phase of the oscillations imposed on these units. The results of the modeling show that the input pattern closest to the stored pattern exhibits the fastest convergence time to stable synchronization behavior. In this way, networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating “materials that compute.” PMID:27617290

  7. Rapid Pattern Recognition of Three Dimensional Objects Using Parallel Processing Within a Hierarchy of Hexagonal Grids

    NASA Astrophysics Data System (ADS)

    Tang, Haojun

    1995-01-01

    This thesis describes using parallel processing within a hierarchy of hexagonal grids to achieve rapid recognition of patterns. A seven-pixel basic hexagonal neighborhood, a sixty-one-pixel superneighborhood and pyramids of a 2-to-4 area ratio are employed. The hexagonal network achieves improved accuracy over the square network for object boundaries. The hexagonal grid with less directional sensitivity is a better approximation of the human vision grid, is more suited to natural scenes than the square grid and avoids the 4-neighbor/8-neighbor problem. Parallel processing in image analysis saves considerable time versus the traditional line-by-line method. Hexagonal parallel processing combines the optimum hexagonal geometry with the parallel structure. Our work has surveyed behavior and internal properties to construct the image on the different level of hexagonal pixel grids in a parallel computation scheme. A computer code has been developed to detect edges of digital images of real objects taken with a CCD camera within a hexagonal grid at any level. The algorithm uses the differences of the local gray level and those of its six neighbors, and is able to determine the boundary of a digital image in parallel. Also a series of algorithms and techniques have been built up to manage edge linking, feature extraction, etc. The digital images obtained from the improved CRS digital image processing system are a good approximation to the images which would be obtained with a real physical hexagonal grid. We envision that our work done within this little-known area will have some important applications in real-time machine vision. A parallel two-layer hexagonal-array retina has been designed to do pattern recognition using simple operations such as differencing, rationing, thresholding, etc. which may occur in the human retina and other biological vision systems.

  8. Multiclass pattern recognition using adaptive correlation filters with complex constraints

    NASA Astrophysics Data System (ADS)

    Diaz-Ramirez, Victor H.; Campos-Trujillo, Oliver G.; Kober, Vitaly; Aguilar-Gonzalez, Pablo M.

    2012-03-01

    An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.

  9. A star pattern recognition algorithm for autonomous attitude determination

    NASA Technical Reports Server (NTRS)

    Van Bezooijen, R. W. H.

    1990-01-01

    The star-pattern recognition algorithm presented allows the advanced Full-sky Autonomous Star Tracker (FAST) device, such as the projected ASTROS II system of the Mariner Mark II planetary spacecraft, to reliably ascertain attitude about all three axes. An ASTROS II-based FAST, possessing an 11.5 x 11.5 deg field of view and 8-arcsec accuracy, can when integrated with an all-sky data base of 4100 guide stars determine its attitude in about 1 sec, with a success rate close to 100 percent. The present recognition algorithm can also be used for automating the acquisition of celestial targets by astronomy telescopes, autonomously updating the attitude of gyro-based attitude control systems, and automating ground-based attitude reconstruction.

  10. Automated target recognition and tracking using an optical pattern recognition neural network

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1991-01-01

    The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

  11. Morphological Characterization of Mycobacterium tuberculosis in a MODS Culture for an Automatic Diagnostics through Pattern Recognition

    PubMed Central

    Alva, Alicia; Aquino, Fredy; Gilman, Robert H.; Olivares, Carlos; Requena, David; Gutiérrez, Andrés H.; Caviedes, Luz; Coronel, Jorge; Larson, Sandra; Sheen, Patricia; Moore, David A. J.; Zimic, Mirko

    2013-01-01

    Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment. PMID:24358227

  12. Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

    PubMed

    Alva, Alicia; Aquino, Fredy; Gilman, Robert H; Olivares, Carlos; Requena, David; Gutiérrez, Andrés H; Caviedes, Luz; Coronel, Jorge; Larson, Sandra; Sheen, Patricia; Moore, David A J; Zimic, Mirko

    2013-01-01

    Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

  13. Electronic system with memristive synapses for pattern recognition

    NASA Astrophysics Data System (ADS)

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-Geun

    2015-05-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

  14. Optimizing automated gas turbine fault detection using statistical pattern recognition

    NASA Astrophysics Data System (ADS)

    Loukis, E.; Mathioudakis, K.; Papailiou, K.

    1992-06-01

    A method enabling the automated diagnosis of Gas Turbine Compressor blade faults, based on the principles of statistical pattern recognition is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurements data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurements data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an Industrial Gas Turbine while extension to other aspects of Fault Diagnosis is discussed.

  15. Electronic system with memristive synapses for pattern recognition

    PubMed Central

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-geun

    2015-01-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction. PMID:25941950

  16. Vibration analysis by digital speckle pattern shearing interferometry

    NASA Astrophysics Data System (ADS)

    Steinchen, Wolfgang; Yang, Lian Xiang; Kupfer, Gerhard; Maeckel, Peter; Thiemich, Anderas

    1997-09-01

    Digital speckle pattern shearing interferometry is described as a robust measuring method due to its simple optical setup and the insensitivity against ambient noise. It has been sued in industry for nondestructive testing and strain measuring. This paper explores the possibilities for vibration analysis using digital speckle pattern shearing interferometry. The measuring device performing both time- average and stroboscopic methods is described. The time average digital speckle patten shearing interferometry in conjunction with the stroboscopic technique is suited well for both qualitative and quantitative vibration analyses. The determination of dynamic deformation and strain fields form the phase map of shearogram is demonstrated, and some preliminary results are shown.

  17. Recognition of dichotic digits under pre-cued and post-cued response conditions in young and elderly listeners.

    PubMed

    Strouse, A; Wilson, R H; Brush, N

    2000-06-01

    Dichotic listening was evaluated in pre-cued and post-cued response conditions using a hierarchical set of one-, two- and three-pair dichotic digit materials. Thirty young adults (mean age 29.1 years) with normal hearing, and 30 older adults in the 60-79-year age range (mean age 68.7 years) with mild-to-moderate sensorineural hearing loss were evaluated. Several patterns of performance were observed. First, recognition performance in the pre-cued condition was better than recognition performance in the post-cued condition for one-, two- and three-pair digits for both age groups. Second, there was a right ear advantage in pre- and post-cued response conditions for both age groups. In the pre-cued condition, the right ear advantage was small owing to ceiling effects associated with ease of the listening task. In the post-cued condition, recognition performance decreased as a function of age, and left ear scores decreased faster than right ear scores, resulting in a larger right ear advantage in the 60-79-year group. Third, as the complexity of the listening task increased from easy (one-pair) to difficult (three-pairs), there was a corresponding decrease in recognition performance for both age groups. The increase in the difference in performance on easy and difficult tasks became larger as a function of age.

  18. Pattern-Recognition Algorithm for Locking Laser Frequency

    NASA Technical Reports Server (NTRS)

    Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

    2006-01-01

    A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

  19. Collocation and Pattern Recognition Effects on System Failure Remediation

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.; Press, Hayes N.

    2007-01-01

    Previous research found that operators prefer to have status, alerts, and controls located on the same screen. Unfortunately, that research was done with displays that were not designed specifically for collocation. In this experiment, twelve subjects evaluated two displays specifically designed for collocating system information against a baseline that consisted of dial status displays, a separate alert area, and a controls panel. These displays differed in the amount of collocation, pattern matching, and parameter movement compared to display size. During the data runs, subjects kept a randomly moving target centered on a display using a left-handed joystick and they scanned system displays to find a problem in order to correct it using the provided checklist. Results indicate that large parameter movement aided detection and then pattern recognition is needed for diagnosis but the collocated displays centralized all the information subjects needed, which reduced workload. Therefore, the collocated display with large parameter movement may be an acceptable display after familiarization because of the possible pattern recognition developed with training and its use.

  20. Multi-texture local ternary pattern for face recognition

    NASA Astrophysics Data System (ADS)

    Essa, Almabrok; Asari, Vijayan

    2017-05-01

    In imagery and pattern analysis domain a variety of descriptors have been proposed and employed for different computer vision applications like face detection and recognition. Many of them are affected under different conditions during the image acquisition process such as variations in illumination and presence of noise, because they totally rely on the image intensity values to encode the image information. To overcome these problems, a novel technique named Multi-Texture Local Ternary Pattern (MTLTP) is proposed in this paper. MTLTP combines the edges and corners based on the local ternary pattern strategy to extract the local texture features of the input image. Then returns a spatial histogram feature vector which is the descriptor for each image that we use to recognize a human being. Experimental results using a k-nearest neighbors classifier (k-NN) on two publicly available datasets justify our algorithm for efficient face recognition in the presence of extreme variations of illumination/lighting environments and slight variation of pose conditions.

  1. Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

    NASA Technical Reports Server (NTRS)

    Restrepo, Carolina; Hurtado, John E.

    2011-01-01

    The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

  2. Pattern recognition for predictive, preventive, and personalized medicine in cancer.

    PubMed

    Cheng, Tingting; Zhan, Xianquan

    2017-03-01

    Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multi-parameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.

  3. Pattern recognition tool based on complex network-based approach

    NASA Astrophysics Data System (ADS)

    Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

    2013-02-01

    This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

  4. A statistical pattern recognition paradigm for structural health monitoring

    SciTech Connect

    Farrar, C. R.; Sohn, H.; Park, G. H.

    2004-01-01

    The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's current or future performance. Our approach is to address the SHM problem in the context of a statistical pattern recognition paradigm (Farrar, Nix and Doebling, 2001). In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition, (3) Feature Extraction, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from 'real-world' structures, it quickly becomes apparent that data cleansing, normalization, fusion and compression, which can be implemented with either hardware or software, are inherent in Parts 2-4 of this paradigm. The authors believe that all approaches to SHM, as well as all traditional non-destructive evaluation procedures (e.g. ultrasonic inspection, acoustic emissions, active thermography) can be cast in the context of this statistical pattern recognition paradigm. It should be noted that the statistical modeling portion of the structural health monitoring process has received the least attention in the technical literature. The algorithms used in statistical model development usually fall into the three categories of group classification, regression analysis or outlier detection. The ability to use a particular statistical procedure from one of these categories will depend on the availability of data from both an undamaged and damaged structure. This paper will discuss each portion of the SHM statistical pattern recognition paradigm.

  5. Making use of longitudinal information in pattern recognition

    PubMed Central

    Lythgoe, David J.; Williams, Steven C.R.; Jokisch, Martha; Mönninghoff, Christoph; Streffer, Johannes; Jöckel, Karl‐Heinz; Weimar, Christian; Marquand, Andre F.

    2016-01-01

    Abstract Longitudinal designs are widely used in medical studies as a means of observing within‐subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross‐sectional style analysis. This article presents a principal component analysis‐based feature construction method that uses longitudinal high‐dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time‐points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross‐sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole‐brain structural magnetic resonance image‐based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385–4404, 2016. © 2016 Wiley Periodicals, Inc. PMID:27451934

  6. Pattern recognition and image processing for environmental monitoring

    NASA Astrophysics Data System (ADS)

    Siddiqui, Khalid J.; Eastwood, DeLyle

    1999-12-01

    Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

  7. A pattern recognition and data analysis method for maintenance management

    NASA Astrophysics Data System (ADS)

    García Márquez, Fausto Pedro; Chacón Muñoz, Jesús Miguel

    2012-06-01

    This article presents a pattern recognition method based on grouping by linear relationship a set of faults. The majority of faults can be detected, but only a few experiments can be identified. The algorithm called Principal Component Analysis (PCA) is employed together with the statistical parameters of the signals for detecting and identifying the faults. PCA technique is utilised for modifying dataset reducing the coordinate system, which must be correlated, by linear transformation, into a smaller set of uncorrelated variables called 'principal components'. The signals analysed were the current and force signals in normal-to-reverse and reverse-to-normal directions of the system.

  8. Innate Pattern Recognition and Categorization in a Jumping Spider

    PubMed Central

    Dolev, Yinnon; Nelson, Ximena J.

    2014-01-01

    The East African jumping spider Evarcha culicivora feeds indirectly on vertebrate blood by preferentially preying upon blood-fed Anopheles mosquitoes, the vectors of human malaria1, using the distinct resting posture and engorged abdomen characteristic of these specific prey as key elements for their recognition. To understand perceptual categorization of objects by these spiders, we investigated their predatory behavior toward different digital stimuli - abstract ‘stick figure’ representations of Anopheles constructed solely by known key identification elements, disarranged versions of these, as well as non-prey items and detailed images of alternative prey. We hypothesized that the abstract images representing Anopheles would be perceived as potential prey, and would be preferred to those of non-preferred prey. Spiders perceived the abstract stick figures of Anopheles specifically as their preferred prey, attacking them significantly more often than non-preferred prey, even when the comprising elements of the Anopheles stick figures were disarranged and disconnected from each other. However, if the relative angles between the elements of the disconnected stick figures of Anopheles were altered, the otherwise identical set of elements was no longer perceived as prey. These data show that E. culicivora is capable of making discriminations based on abstract concepts, such as the hypothetical angle formed by discontinuous elements. It is this inter-element angle rather than resting posture that is important for correct identification of Anopheles. Our results provide a glimpse of the underlying processes of object recognition in animals with minute brains, and suggest that these spiders use a local processing approach for object recognition, rather than a holistic or global approach. This study provides an excellent basis for a comparative analysis on feature extraction and detection by animals as diverse as bees and mammals. PMID:24893306

  9. Recognition and inference of crevice processing on digitized paintings

    NASA Astrophysics Data System (ADS)

    Karuppiah, S. P.; Srivatsa, S. K.

    2013-03-01

    This paper is designed to detect and removal of cracks on digitized paintings. The cracks are detected by threshold. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median radial basis function neural network on hue and saturation data, Semi-automatic procedure based on region growing. Finally, crack is filled using wiener filter. The paper is well designed in such a way that most of the cracks on digitized paintings have identified and removed. The paper % of betterment is 90%. This paper helps us to perform not only on digitized paintings but also the medical images and bmp images. This paper is implemented by Mat Lab.

  10. A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation.

    PubMed

    Macho, Alberto P; Schwessinger, Benjamin; Ntoukakis, Vardis; Brutus, Alexandre; Segonzac, Cécile; Roy, Sonali; Kadota, Yasuhiro; Oh, Man-Ho; Sklenar, Jan; Derbyshire, Paul; Lozano-Durán, Rosa; Malinovsky, Frederikke Gro; Monaghan, Jacqueline; Menke, Frank L; Huber, Steven C; He, Sheng Yang; Zipfel, Cyril

    2014-03-28

    Innate immunity relies on the perception of pathogen-associated molecular patterns (PAMPs) by pattern-recognition receptors (PRRs) located on the host cell's surface. Many plant PRRs are kinases. Here, we report that the Arabidopsis receptor kinase EF-TU RECEPTOR (EFR), which perceives the elf18 peptide derived from bacterial elongation factor Tu, is activated upon ligand binding by phosphorylation on its tyrosine residues. Phosphorylation of a single tyrosine residue, Y836, is required for activation of EFR and downstream immunity to the phytopathogenic bacterium Pseudomonas syringae. A tyrosine phosphatase, HopAO1, secreted by P. syringae, reduces EFR phosphorylation and prevents subsequent immune responses. Thus, host and pathogen compete to take control of PRR tyrosine phosphorylation used to initiate antibacterial immunity.

  11. DSP-Based dual-polarity mass spectrum pattern recognition for bio-detection

    SciTech Connect

    Riot, V; Coffee, K; Gard, E; Fergenson, D; Ramani, S; Steele, P

    2006-04-21

    The Bio-Aerosol Mass Spectrometry (BAMS) instrument analyzes single aerosol particles using a dual-polarity time-of-flight mass spectrometer recording simultaneously spectra of thirty to a hundred thousand points on each polarity. We describe here a real-time pattern recognition algorithm developed at Lawrence Livermore National Laboratory that has been implemented on a nine Digital Signal Processor (DSP) system from Signatec Incorporated. The algorithm first preprocesses independently the raw time-of-flight data through an adaptive baseline removal routine. The next step consists of a polarity dependent calibration to a mass-to-charge representation, reducing the data to about five hundred to a thousand channels per polarity. The last step is the identification step using a pattern recognition algorithm based on a library of known particle signatures including threat agents and background particles. The identification step includes integrating the two polarities for a final identification determination using a score-based rule tree. This algorithm, operating on multiple channels per-polarity and multiple polarities, is well suited for parallel real-time processing. It has been implemented on the PMP8A from Signatec Incorporated, which is a computer based board that can interface directly to the two one-Giga-Sample digitizers (PDA1000 from Signatec Incorporated) used to record the two polarities of time-of-flight data. By using optimized data separation, pipelining, and parallel processing across the nine DSPs it is possible to achieve a processing speed of up to a thousand particles per seconds, while maintaining the recognition rate observed on a non-real time implementation. This embedded system has allowed the BAMS technology to improve its throughput and therefore its sensitivity while maintaining a large dynamic range (number of channels and two polarities) thus maintaining the systems specificity for bio-detection.

  12. Gene prediction by pattern recognition and homology search

    SciTech Connect

    Xu, Y.; Uberbacher, E.C.

    1996-05-01

    This paper presents an algorithm for combining pattern recognition-based exon prediction and database homology search in gene model construction. The goal is to use homologous genes or partial genes existing in the database as reference models while constructing (multiple) gene models from exon candidates predicted by pattern recognition methods. A unified framework for gene modeling is used for genes ranging from situations with strong homology to no homology in the database. To maximally use the homology information available, the algorithm applies homology on three levels: (1) exon candidate evaluation, (2) gene-segment construction with a reference model, and (3) (complete) gene modeling. Preliminary testing has been done on the algorithm. Test results show that (a) perfect gene modeling can be expected when the initial exon predictions are reasonably good and a strong homology exists in the database; (b) homology (not necessarily strong) in general helps improve the accuracy of gene modeling; (c) multiple gene modeling becomes feasible when homology exists in the database for the involved genes.

  13. Resolving the limb position effect in myoelectric pattern recognition.

    PubMed

    Fougner, Anders; Scheme, Erik; Chan, Adrian D C; Englehart, Kevin; Stavdahl, Oyvind

    2011-12-01

    Reported studies on pattern recognition of electromyograms (EMG) for the control of prosthetic devices traditionally focus on classification accuracy of signals recorded in a laboratory. The difference between the constrained nature in which such data are often collected and the unpredictable nature of prosthetic use is an example of the semantic gap between research findings and a viable clinical implementation. In this paper, we demonstrate that the variations in limb position associated with normal use can have a substantial impact on the robustness of EMG pattern recognition, as illustrated by an increase in average classification error from 3.8% to 18%. We propose to solve this problem by: 1) collecting EMG data and training the classifier in multiple limb positions and by 2) measuring the limb position with accelerometers. Applying these two methods to data from ten normally limbed subjects, we reduce the average classification error from 18% to 5.7% and 5.0%, respectively. Our study shows how sensor fusion (using EMG and accelerometers) may be an efficient method to mitigate the effect of limb position and improve classification accuracy.

  14. Evaluation of Anomaly Detection Method Based on Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Fontugne, Romain; Himura, Yosuke; Fukuda, Kensuke

    The number of threats on the Internet is rapidly increasing, and anomaly detection has become of increasing importance. High-speed backbone traffic is particularly degraded, but their analysis is a complicated task due to the amount of data, the lack of payload data, the asymmetric routing and the use of sampling techniques. Most anomaly detection schemes focus on the statistical properties of network traffic and highlight anomalous traffic through their singularities. In this paper, we concentrate on unusual traffic distributions, which are easily identifiable in temporal-spatial space (e.g., time/address or port). We present an anomaly detection method that uses a pattern recognition technique to identify anomalies in pictures representing traffic. The main advantage of this method is its ability to detect attacks involving mice flows. We evaluate the parameter set and the effectiveness of this approach by analyzing six years of Internet traffic collected from a trans-Pacific link. We show several examples of detected anomalies and compare our results with those of two other methods. The comparison indicates that the only anomalies detected by the pattern-recognition-based method are mainly malicious traffic with a few packets.

  15. Pattern recognition of satellite cloud imagery for improved weather prediction

    NASA Technical Reports Server (NTRS)

    Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

    1986-01-01

    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.

  16. Visual pattern recognition network: its training algorithm and its optoelectronic architecture

    NASA Astrophysics Data System (ADS)

    Wang, Ning; Liu, Liren

    1996-07-01

    A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition.

  17. Unsupervised learning of digit recognition using spike-timing-dependent plasticity

    PubMed Central

    Diehl, Peter U.; Cook, Matthew

    2015-01-01

    In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

  18. Detection of deleted patterns in handwritten digits using topological and geometrical image features

    NASA Astrophysics Data System (ADS)

    Suwa, Misako; Naoi, Satoshi; Hotta, Yoshinobu

    1998-04-01

    One of the critical problems of an off-line handwritten character reader system is determining which patterns to read and which to ignore, as a form or a document contains not only characters but also spots and deletions. As long as they don't fit conditions for rejection, they cause recognition errors. Particularly, patterns of deleted single-character are difficult to be distinguished from a character, because their sizes are almost the same as that of a character and their shapes have variety. In this article, we proposed a method to detect such deletions in handwritten digits using topological and geometrical image- features suitable for detecting them; Eular number, pixel density, number of endpoint, maximum crossing counts and number of peaks of histogram. For precise detection, thresholds of the image features are adaptively selected according to their recognition results.

  19. Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition

    PubMed Central

    Hansen, Mirko; Zahari, Finn; Ziegler, Martin; Kohlstedt, Hermann

    2017-01-01

    The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al2O3/NbxOy/Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3 tunnel barrier and a 2.5 mm thick NbxOy memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I–V non-linearity might avoid the need for selector devices in crossbar array structures. PMID:28293164

  20. Principal Component Analysis for pattern recognition in volcano seismic spectra

    NASA Astrophysics Data System (ADS)

    Unglert, Katharina; Jellinek, A. Mark

    2016-04-01

    Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we have developed a pattern recognition technique based on a combination of Principal Component Analysis and hierarchical clustering applied to volcano seismic spectra. This technique can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. Preliminary results from applying our method to volcanic tremor from a range of volcanoes including K¯ı lauea, Okmok, Pavlof, and Redoubt suggest that spectral patterns from K¯ı lauea and Okmok are similar, whereas at Pavlof and Redoubt spectra have their own, distinct patterns.

  1. Situation-orientated recognition of tactical patterns in volleyball.

    PubMed

    Jäger, Jörg M; Schöllhorn, Wolfgang I

    2007-10-01

    One important factor for effective operations in team sports is the team tactical behaviour. Many suggestions about appropriate players' positions in different attack or defence situations have been made. The aims of this study were to develop a classification of offensive and defensive behaviours and to identify team-specific tactical patterns in international women's volleyball. Both the classification and identification of tactical patterns is done by means of a hierarchical cluster analysis. Clusters are formed on the basis of similarities in the players' positions on the court. Time continuous data of the movements, including the start and end points during a pass from the setter, are analysed. Results show team-specific patterns of defensive moves with assessment rates of up to 80%. Furthermore, the recognition of match situations illustrates a clear classification of attack and defence situations and even within different defence conditions (approximately 100%). Thus, this approach to team tactical analysis yields classifications of selected offensive and defensive strategies as well as an identification of tactical patterns of different national teams in standardized situations. The results lead us to question training concepts that assume a team-independent optimal strategy with respect to the players' positions in team sports.

  2. Pharmacology and therapeutic potential of pattern recognition receptors.

    PubMed

    Paul-Clark, M J; George, P M; Gatheral, T; Parzych, K; Wright, W R; Crawford, D; Bailey, L K; Reed, D M; Mitchell, J A

    2012-08-01

    Pharmacologists have used pathogen-associated molecular patterns (PAMPs), such as lipopolysaccharide (LPS) for decades as a stimulus for studying mediators involved in inflammation and for the screening of anti-inflammatory compounds. However, in the view of immunologists, LPS was too non-specific for studying the mechanisms of immune signalling in infection and inflammation, as no receptors had been identified. This changed in the late 1990s with the discovery of the Toll-like receptors. These 'pattern recognition receptors' (PRRs) were able to recognise highly conserved sequences, the so called pathogen associated molecular patterns (PAMPs) present in or on pathogens. This specificity of particular PAMPs and their newly defined receptors provided a common ground between pharmacologists and immunologists for the study of inflammation. PRRs also recognise endogenous agonists, the so called danger-associated molecular patterns (DAMPs), which can result in sterile inflammation. The signalling pathways and ligands of many PRRs have now been characterised and there is no doubt that this rich vein of research will aid the discovery of new therapeutics for infectious conditions and chronic inflammatory disease.

  3. Albedo Pattern Recognition and Time-Series Analyses in Malaysia

    NASA Astrophysics Data System (ADS)

    Salleh, S. A.; Abd Latif, Z.; Mohd, W. M. N. Wan; Chan, A.

    2012-07-01

    Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000-2009) MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools). There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI) and aerosol optical depth (AOD). There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high negative linear

  4. Effect of order bias on the recognition of dichotic digits in young and elderly listeners.

    PubMed

    Strouse, A; Wilson, R H; Brush, N

    2000-01-01

    Dichotic listening was evaluated in free-recall and directed-recall (pre-cued, post-cued) response conditions using interleaved one-, two-, and three-pair dichotic digit materials. In the free-recall condition, the subjects recalled in any order the digits presented. In the directed-recall condition, a response task was examined where subjects recalled all digits presented to the cued ear (pre- or post-cued) followed by the digits presented to the opposite (non-cued) ear. Thirty 20- to 29-year-old adults with normal hearing and 30 60- to 79-year-old adults with mild-to-moderate sensorineural hearing loss were evaluated. In all conditions, performance by the younger listeners was better than performance by the elderly listeners. As the difficulty of the dichotic digit task increased, recognition performance decreased. The recognition performance of elderly listeners was more affected by increases in the difficulty of the stimulus materials as compared to the younger listeners. In the free-recall condition, there was a right-ear advantage for both age groups. When instructional bias was imposed, the results favored the ear of instructed bias. The differences in recognition performance between young and elderly listeners likely reflect differences in the difficulty of the dichotic digit test conditions and variations in the demand on auditory memory.

  5. Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb

    NASA Astrophysics Data System (ADS)

    Baird, Bill

    1986-10-01

    A mathematical model of the process of pattern recognition in the first olfactory sensory cortex of the rabbit is presented. It explains the formation and alteration of spatial patterns in neural activity observed experimentally during classical Pavlovian conditioning. On each inspiration of the animal, a surge of receptor input enters the olfactory bulb. EEG activity recorded at the surface of the bulb undergoes a transition from a low amplitude background state of temporal disorder to coherent oscillation. There is a distinctive spatial pattern of rms amplitude in this oscillation which changes reliably to a second pattern during each successful recognition by the animal of a conditioned stimulus odor. When a new odor is paired as conditioned stimulus, these patterns are replaced by new patterns that stabilize as the animal adapts to the new environment. I will argue that a unification of the theories of pattern formation and associative memory is required to account for these observations. This is achieved in a model of the bulb as a discrete excitable medium with spatially inhomogeneous coupling expressed by a connection matrix. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of competing unstable oscillatory modes. These may be created in the system by proper coupling and selectively evoked by specific classes of inputs. This allows a view of limit cycle attractors as “stored” fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

  6. The application of pattern recognition in the automatic classification of microscopic rock images

    NASA Astrophysics Data System (ADS)

    Młynarczuk, Mariusz; Górszczyk, Andrzej; Ślipek, Bartłomiej

    2013-10-01

    The classification of rocks is an inherent part of modern geology. The manual identification of rock samples is a time-consuming process, and-due to the subjective nature of human judgement-burdened with risk. In the course of the study discussed in the present paper, the authors investigated the possibility of automating this process. During the study, nine different rock samples were used. Their digital images were obtained from thin sections, with a polarizing microscope. These photographs were subsequently classified in an automatic manner, by means of four pattern recognition methods: the nearest neighbor algorithm, the K-nearest neighbor, the nearest mode algorithm, and the method of optimal spherical neighborhoods. The effectiveness of these methods was tested in four different color spaces: RGB, CIELab, YIQ, and HSV. The results of the study show that the automatic recognition of the discussed rock types is possible. The study also revealed that, if the CIELab color space and the nearest neighbor classification method are used, the rock samples in question are classified correctly, with the recognition levels of 99.8%.

  7. An investigation of optical composite filters for pattern recognition

    NASA Astrophysics Data System (ADS)

    Li, Chun-Te

    With the technological advancements in high speed CCD cameras and high resolution spatial light modulators (SLMs), joint transform correlators (JTCs) have obtained more attention in optical pattern recognition, for two main reasons: they are robust to environmental perturbation and they do not require a prefabricated Fourier domain filter, as does the VanderLugt correlator (VLC). JTCs suffer from poor detection efficiency, however, particularly in multi-target and high background noise environments. This study presents several efforts to improve the JTC's performance for pattern recognition. An optimum training process using the simulated annealing algorithm for construction of multilevel composite function (MCF) filters in the input domain was studied. MCFs are suitable for current SLMs because of their limited dynamic range. We investigated the performance of MCFs to observe the impact of number of levels on distortion invariance and discriminability. A JTC system with position encoding technique, which allows a real- valued or complex-valued function to be displayed on an amplitude-modulated SLM, is also provided for the optical implementation of MCF filters. Due to the existence of the zero-order spectra, JTCs suffer from poor detection efficiency. To alleviate this problem, a simple method of removing the zero-order spectra in a joint transform power spectrum (JTPS) was investigated. We have shown that the nonzero-order JTC (NOJTC) performs better, compared to the conventional JTC (CJTC), in terms of detection and defraction efficiency, pixel utilization and avoidance of false alarms due to inter-object modulation. Simulated and experimental demonstrations has been provided. A practical method for identifying the Synthetic-Aperture Radar (SAR) image with a 360-degree rotation-invariant based on a set of MCF filters was also investigated. The MCFs are synthesized by the simulated annealing algorithm, which are suitable for current SLMs because of the limited

  8. Interaction of Pattern Recognition Receptors with Mycobacterium Tuberculosis.

    PubMed

    Mortaz, Esmaeil; Adcock, Ian M; Tabarsi, Payam; Masjedi, Mohammad Reza; Mansouri, Davood; Velayati, Ali Akbar; Casanova, Jean-Laurent; Barnes, Peter J

    2015-01-01

    Tuberculosis (TB) is considered a major worldwide health problem with 10 million new cases diagnosed each year. Our understanding of TB immunology has become greater and more refined since the identification of Mycobacterium tuberculosis (MTB) as an etiologic agent and the recognition of new signaling pathways modulating infection. Understanding the mechanisms through which the cells of the immune system recognize MTB can be an important step in designing novel therapeutic approaches, as well as improving the limited success of current vaccination strategies. A great challenge in chronic disease is to understand the complexities, mechanisms, and consequences of host interactions with pathogens. Innate immune responses along with the involvement of distinct inflammatory mediators and cells play an important role in the host defense against the MTB. Several classes of pattern recognition receptors (PRRs) are involved in the recognition of MTB including Toll-Like Receptors (TLRs), C-type lectin receptors (CLRs) and Nod-like receptors (NLRs) linked to inflammasome activation. Among the TLR family, TLR1, TLR2, TLR4, and TLR9 and their down-stream signaling proteins play critical roles in the initiation of the immune response in the pathogenesis of TB. The inflammasome pathway is associated with the coordinated release of cytokines such as IL-1β and IL-18 which also play a role in the pathogenesis of TB. Understanding the cross-talk between these signaling pathways will impact on the design of novel therapeutic strategies and in the development of vaccines and immunotherapy regimes. Abnormalities in PRR signaling pathways regulated by TB will affect disease pathogenesis and need to be elucidated. In this review we provide an update on PRR signaling during M. tuberculosis infection and indicate how greater knowledge of these pathways may lead to new therapeutic opportunities.

  9. Digital Badging at The Open University: Recognition for Informal Learning

    ERIC Educational Resources Information Center

    Law, Patrina

    2015-01-01

    Awarding badges to recognise achievement is not a new development. Digital badging now offers new ways to recognise learning and motivate learners, providing evidence of skills and achievements in a variety of formal and informal settings. Badged open courses (BOCs) were piloted in various forms by the Open University (OU) in 2013 to provide a…

  10. Polygon cluster pattern recognition based on new visual distance

    NASA Astrophysics Data System (ADS)

    Shuai, Yun; Shuai, Haiyan; Ni, Lin

    2007-06-01

    The pattern recognition of polygon clusters is a most attention-getting problem in spatial data mining. The paper carries through a research on this problem, based on spatial cognition principle and visual recognition Gestalt principle combining with spatial clustering method, and creates two innovations: First, the paper carries through a great improvement to the concept---"visual distance". In the definition of this concept, not only are Euclid's Distance, orientation difference and dimension discrepancy comprehensively thought out, but also is "similarity degree of object shape" crucially considered. In the calculation of "visual distance", the distance calculation model is built using Delaunay Triangulation geometrical structure. Second, the research adopts spatial clustering analysis based on MST Tree. In the design of pruning algorithm, the study initiates data automatism delamination mechanism and introduces Simulated Annealing Optimization Algorithm. This study provides a new research thread for GIS development, namely, GIS is an intersection principle, whose research method should be open and diverse. Any mature technology of other relative principles can be introduced into the study of GIS, but, they need to be improved on technical measures according to the principles of GIS as "spatial cognition science". Only to do this, can GIS develop forward on a higher and stronger plane.

  11. Face recognition using local gradient binary count pattern

    NASA Astrophysics Data System (ADS)

    Zhao, Xiaochao; Lin, Yaping; Ou, Bo; Yang, Junfeng; Wu, Zhelun

    2015-11-01

    A local feature descriptor, the local gradient binary count pattern (LGBCP), is proposed for face recognition. Unlike some current methods that extract features directly from a face image in the spatial domain, LGBCP encodes the local gradient information of the face's texture in an effective way and provides a more discriminative code than other methods. We compute the gradient information of a face image through convolutions with compass masks. The gradient information is encoded using the local binary count operator. We divide a face into several subregions and extract the distribution of the LGBCP codes from each subregion. Then all the histograms are concatenated into a vector, which is used for face description. For recognition, the chi-square statistic is used to measure the similarity of different feature vectors. Besides directly calculating the similarity of two feature vectors, we provide a weighted matching scheme in which different weights are assigned to different subregions. The nearest-neighborhood classifier is exploited for classification. Experiments are conducted on the FERET, CAS-PEAL, and AR face databases. LGBCP achieves 96.15% on the Fb set of FERET. For CAS-PEAL, LGBCP gets 96.97%, 98.91%, and 90.89% on the aging, distance, and expression sets, respectively.

  12. A novel thermal face recognition approach using face pattern words

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2010-04-01

    A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

  13. The Role of Pattern Recognition Receptors in Intestinal Inflammation

    PubMed Central

    Fukata, Masayuki; Arditi, Moshe

    2013-01-01

    Recognition of microorganisms by pattern recognition receptors (PRRs) is the primary component of innate immunity that is responsible for the maintenance of host-microbial interactions in intestinal mucosa. Disregulation in host-commensal interactions has been implicated as the central pathogenesis of inflammatory bowel disease (IBD), which predisposes to developing colorectal cancer. Recent animal studies have begun to outline some unique physiology and pathology involving each PRR signaling in the intestine. The major roles played by PRRs in the gut appear to be regulation of the number and the composition of commensal bacteria, epithelial proliferation and mucosal permiability in response to epithelial injury. In addition, PRR signaling in lamina propria immune cells may be involved in induction of inflammation in response to invasion of pathogens. Because some PRR-deficient mice have shown variable susceptibility to colitis, the outcome of intestinal inflammation may be modified depending on PRR signaling in epithelial cells, immune cells, and the composition of commensal flora. Through recent findings in animal models of IBD, this review will discuss how abnormal PRR signaling may contribute to the pathogenesis of inflammation and inflammation-associated tumorigenesis in the intestine. PMID:23515136

  14. Auditory Pattern Recognition and Brief Tone Discrimination of Children with Reading Disorders

    ERIC Educational Resources Information Center

    Walker, Marianna M.; Givens, Gregg D.; Cranford, Jerry L.; Holbert, Don; Walker, Letitia

    2006-01-01

    Auditory pattern recognition skills in children with reading disorders were investigated using perceptual tests involving discrimination of frequency and duration tonal patterns. A behavioral test battery involving recognition of the pattern of presentation of tone triads was used in which individual components differed in either frequency or…

  15. Investigation of Optimal Digital Image Correlation Patterns for Deformation Measurement

    NASA Technical Reports Server (NTRS)

    Bomarito, G. F.; Ruggles, T. J.; Hochhalter, J. D.; Cannon, A. H.

    2016-01-01

    Digital image correlation (DIC) relies on the surface texture of a specimen to measure deformation. When the specimen itself has little or no texture, a pattern is applied to the surface which deforms with the specimen and acts as an artificial surface texture. Because the applied pattern has an effect on the accuracy of DIC, an ideal pattern is sought for which the error introduced into DIC measurements is minimal. In this work, a study is performed in which several DIC pattern quality metrics from the literature are correlated to DIC measurement error. The resulting correlations give insight on the optimality of DIC patterns in general. Optimizations are then performed to produce patterns which are well suited for DIC. These patterns are tested to show their relative benefits. Chief among these benefits are a reduction in error of approximately 30 with respect to a randomly generated pattern.

  16. Pattern Recognition Receptor–Dependent Mechanisms of Acute Lung Injury

    PubMed Central

    Xiang, Meng; Fan, Jie

    2010-01-01

    Acute lung injury (ALI) that clinically manifests as acute respiratory distress syndrome is caused by an uncontrolled systemic inflammatory response resulting from clinical events including sepsis, major surgery and trauma. Innate immunity activation plays a central role in the development of ALI. Innate immunity is activated through families of related pattern recognition receptors (PRRs), which recognize conserved microbial motifs or pathogen-associated molecular patterns (PAMPs). Toll-like receptors were the first major family of PRRs discovered in mammals. Recently, NACHT–leucine-rich repeat (LRR) receptors and retinoic acid–inducible gene–like receptors have been added to the list. It is now understood that in addition to recognizing infectious stimuli, both Toll-like receptors and NACHT-LRR receptors can also respond to endogenous molecules released in response to stress, trauma and cell damage. These molecules have been termed damage-associated molecular patterns (DAMPs). It has been clinically observed for a long time that infectious and noninfectious insults initiate inflammation, so confirmation of overlapping receptor-signal pathways of activation between PAMPs and DAMPs is no surprise. This review provides an overview of the PRR-dependent mechanisms of ALI and clinical implication. Modification of PRR pathways is likely to be a logical therapeutic target for ALI/acute respiratory distress syndrome. PMID:19949486

  17. A Gesture Recognition System for Detecting Behavioral Patterns of ADHD.

    PubMed

    Bautista, Miguel Ángel; Hernández-Vela, Antonio; Escalera, Sergio; Igual, Laura; Pujol, Oriol; Moya, Josep; Violant, Verónica; Anguera, María T

    2016-01-01

    We present an application of gesture recognition using an extension of dynamic time warping (DTW) to recognize behavioral patterns of attention deficit hyperactivity disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either Gaussian mixture models or an approximation of convex hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intraclass gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioral patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multimodal dataset (RGB plus depth) of ADHD children recordings with behavioral patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.

  18. Geometry Of Discrete Sets With Applications To Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Sinha, Divyendu

    1990-03-01

    In this paper we present a new framework for discrete black and white images that employs only integer arithmetic. This framework is shown to retain the essential characteristics of the framework for Euclidean images. We propose two norms and based on them, the permissible geometric operations on images are defined. The basic invariants of our geometry are line images, structure of image and the corresponding local property of strong attachment of pixels. The permissible operations also preserve the 3x3 neighborhoods, area, and perpendicularity. The structure, patterns, and the inter-pattern gaps in a discrete image are shown to be conserved by the magnification and contraction process. Our notions of approximate congruence, similarity and symmetry are similar, in character, to the corresponding notions, for Euclidean images [1]. We mention two discrete pattern recognition algorithms that work purely with integers, and which fit into our framework. Their performance has been shown to be at par with the performance of traditional geometric schemes. Also, all the undesired effects of finite length registers in fixed point arithmetic that plague traditional algorithms, are non-existent in this family of algorithms.

  19. [Application of phenological pattern recognition in ecological dynamic forecasting].

    PubMed

    Pei, Tiefan; Jin, Changiie

    2005-09-01

    This paper described the principles, methods, and procedures of ecological dynamic forecasting by the automation techniques of pattern recognition and mathematical logic judgment on the basis of phenological data and model output maps from T42L9 numerical weather prediction model. This new forecasting method proposed on the basis of modern meteorology and automation techniques enables the classic phenology to apply to a new field ecological forecasting. It enables phenological forecasting to develop from single-station forecasting stage to regional forecasting stage, which is greatly corresponded to the development stage from single station forecasting stage to synoptic stage in weather forecasting, and enables agro-meteorological forecasting to develop from qualitative and statistical forecasting stage to ecological dynamic forecasting stage. With this new qualitative forecasting method, both the predicted objective and predictors are of considerable bio-physical interests. The ecological dynamic forecasting method could be applied to crop sowing, crop growth, irrigation and fertilization, and diseases and pests

  20. [New immunology--immunology of pattern recognition receptors].

    PubMed

    Lebedev, K A; Poniakina, I D

    2006-01-01

    Pattern recognition receptors (PRRs) have been found on all cells of the body--cells of the innate and adaptive immune systems, epithelial and endothelial cells, keratinocytes, etc. PRRs can recognize specific molecular structures of microorganisms as well as allergens and other substances. The interaction with ligands of foreign microorganisms activates PRRs, after which host cells start to produce cytokines to both specifically activate innate immunity and to control adaptive immune reactions. On the other hand, no immune response develops against microorganisms of the normal microflora. Practically, the development of all immune responses is controlled by PRRs. These responses start in epithelial cells, skin cells, and vascular epithelial cells, which meet alien first. The immune system uses these cells to control the composition of normal microflora. Accordingly, the definition of immune system functions should be complemented by the regulation of body's microflora in addition to the protection from alien and altered self.

  1. Recognition of lipopolysaccharide pattern by TLR4 complexes

    PubMed Central

    Park, Beom Seok; Lee, Jie-Oh

    2013-01-01

    Lipopolysaccharide (LPS) is a major component of the outer membrane of Gram-negative bacteria. Minute amounts of LPS released from infecting pathogens can initiate potent innate immune responses that prime the immune system against further infection. However, when the LPS response is not properly controlled it can lead to fatal septic shock syndrome. The common structural pattern of LPS in diverse bacterial species is recognized by a cascade of LPS receptors and accessory proteins, LPS binding protein (LBP), CD14 and the Toll-like receptor4 (TLR4)–MD-2 complex. The structures of these proteins account for how our immune system differentiates LPS molecules from structurally similar host molecules. They also provide insights useful for discovery of anti-sepsis drugs. In this review, we summarize these structures and describe the structural basis of LPS recognition by LPS receptors and accessory proteins. PMID:24310172

  2. Online Pattern Recognition for the ALICE High Level Trigger

    NASA Astrophysics Data System (ADS)

    Lindenstruth, V.; Loizides, C.; Rohrich, D.; Skaali, B.; Steinbeck, T.; Stock, R.; Tilsner, H.; Ullaland, K.; Vestbo, A.; Vik, T.

    2004-06-01

    The ALICE high level trigger has to process data online, in order to select interesting (sub)events, or to compress data efficiently by modeling techniques. Focusing on the main data source, the time projection chamber (TPC), we present two pattern recognition methods under investigation: a sequential approach (cluster finder and track follower) and an iterative approach (track candidate finder and cluster deconvoluter). We show, that the former is suited for pp and low multiplicity PbPb collisions, whereas the latter might be applicable for high multiplicity PbPb collisions of dN/dy>3000. Based on the developed tracking schemes we show that using modeling techniques, a compression factor of around 10 might be achievable.

  3. Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Downie, John D.; Tucker, Deanne (Technical Monitor)

    1994-01-01

    Logarithmic processing of images with multiplicative noise characteristics can be utilized to transform the image into one with an additive noise distribution. This simplifies subsequent image processing steps for applications such as image restoration or correlation for pattern recognition. One particularly common form of multiplicative noise is speckle, for which the logarithmic operation not only produces additive noise, but also makes it of constant variance (signal-independent). We examine the optical transmission properties of some bacteriorhodopsin films here and find them well suited to implement such a pointwise logarithmic transformation optically in a parallel fashion. We present experimental results of the optical conversion of speckle images into transformed images with additive, signal-independent noise statistics using the real-time photochromic properties of bacteriorhodopsin. We provide an example of improved correlation performance in terms of correlation peak signal-to-noise for such a transformed speckle image.

  4. Special Guest Editorial: Optical Pattern Recognition: An Overview

    NASA Astrophysics Data System (ADS)

    VanderLugt, A.

    1984-12-01

    During a recent trip I waited for my suitcase after a heavily loaded flight from Atlanta to Los Angeles. As the various baggage items appeared on the conveyor, I reflected on the general problem of pattern recognition. The bags came in various shapes, colors, sizes, and orientations: my eyes casually scanned them as they appeared, and I found that I could quickly dismiss those that did not match my mental image of my own bag. I immediately recognized my bag when it appeared (it does have some distinctive scars after years of service) and resumed my trip to the hotel. While I was waiting, I also found time to observe the people gathered in the baggage area to see if there was anyone I recognized. Again, I was reminded of how subtle differences in facial expression, posture, and general body style allow us to instantly recognize people we know.

  5. Pattern recognition issues on anisotropic smoothed particle hydrodynamics

    NASA Astrophysics Data System (ADS)

    Pereira Marinho, Eraldo

    2014-03-01

    This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

  6. Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Downie, John D.; Tucker, Deanne (Technical Monitor)

    1994-01-01

    Logarithmic processing of images with multiplicative noise characteristics can be utilized to transform the image into one with an additive noise distribution. This simplifies subsequent image processing steps for applications such as image restoration or correlation for pattern recognition. One particularly common form of multiplicative noise is speckle, for which the logarithmic operation not only produces additive noise, but also makes it of constant variance (signal-independent). We examine the optical transmission properties of some bacteriorhodopsin films here and find them well suited to implement such a pointwise logarithmic transformation optically in a parallel fashion. We present experimental results of the optical conversion of speckle images into transformed images with additive, signal-independent noise statistics using the real-time photochromic properties of bacteriorhodopsin. We provide an example of improved correlation performance in terms of correlation peak signal-to-noise for such a transformed speckle image.

  7. Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.

    PubMed

    Kim, Sungho; Yoon, Jinsu; Kim, Hee-Dong; Choi, Sung-Jin

    2015-11-18

    Inspired by the human brain, a neuromorphic system combining complementary metal-oxide semiconductor (CMOS) and adjustable synaptic devices may offer new computing paradigms by enabling massive neural-network parallelism. In particular, synaptic devices, which are capable of emulating the functions of biological synapses, are used as the essential building blocks for an information storage and processing system. However, previous synaptic devices based on two-terminal resistive devices remain challenging because of their variability and specific physical mechanisms of resistance change, which lead to a bottleneck in the implementation of a high-density synaptic device network. Here we report that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change. In addition, using system-level simulations, the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme.

  8. Pattern Recognition in Gamma-Gamma Coincidence Data sets

    NASA Astrophysics Data System (ADS)

    Manatt, D. R.; Barnes, F. L.; Becker, J. A.; Candy, J. V.; Henry, E. A.; Brinkman, M. J.

    1991-10-01

    Considerable amounts of tedious labor are required to manually scan high-resolution 1D slices of two dimensional γ-γ coincident matrices for relevant and exciting structures. This is particularly true when the interesting structures are of weak intensity. We are working on automated search methods for the detection of rotational band structures in the full 2D space using pattern recognition techniques. For nominal sized data sets (1024×1024), however, these techniques only become computationally feasible through the use of Fourier Transform methods. Furthermore the presentation of data matrices as images rather than series of 1D spectra has been shown to be useful. In this paper we will present the data manipulation techniques we have developed.

  9. Fuzzy pattern recognition method for assessing soil erosion.

    PubMed

    Saadatpour, Motahareh; Afshar, Abbas; Afshar, Mohammad Hadi

    2011-09-01

    In this paper a PSIAC-based multi-parameter fuzzy pattern recognition (MPFPR) model is proposed and applied for classifying and ranking the potential soil erosion (PSE). In this approach, standard value matrix is used to define the membership degrees of each catchment to each class and the feature values are used for alternative ranking. The characteristic of PSE for each class is expressed by linguistic variables. The proposed method is straightforward, easy to understand, very practical, and its results may easily be interpreted. To assess the performance of the model, the results of PSIAC MPFPR and original PSIAC method are interpreted and compared with the observed data. It is shown that the proposed approach reflects the fuzzy nature of the soil erosion more efficiently and is quite robust for application in real world cases.

  10. Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

    NASA Astrophysics Data System (ADS)

    Akita, K.; Kuga, H.

    1982-11-01

    We propose a computer method of recognizing blood vessel networks in color ocular fundus images which are used in the mass diagnosis of adult diseases such as hypertension and diabetes. A line detection algorithm is applied to extract the blood vessels, and the skeleton patterns of them are made to analyze and describe their structures. The recognition of line segments of arteries and/or veins in the vessel networks consists of three stages. First, a few segments which satisfy a certain constraint are picked up and discriminated as arteries or veins. This is the initial labeling. Then the remaining unknown ones are labeled by utilizing the physical level knowledge. We propose two schemes for this stage : a deterministic labeling and a probabilistic relaxation labeling. Finally the label of each line segment is checked so as to minimize the total number of labeling contradictions. Some experimental results are also presented.

  11. Pattern recognition by wavelet transforms using macro fibre composites transducers

    NASA Astrophysics Data System (ADS)

    Ruiz de la Hermosa González-Carrato, Raúl; García Márquez, Fausto Pedro; Dimlaye, Vichaar; Ruiz-Hernández, Diego

    2014-10-01

    This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. The wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated with cracks, leaks or corrosion. This issue may not be noticeable when temperatures are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarm levels as a part of a structural health monitoring system.

  12. Pattern recognition receptors and the inflammasome in kidney disease.

    PubMed

    Leemans, Jaklien C; Kors, Lotte; Anders, Hans-Joachim; Florquin, Sandrine

    2014-07-01

    Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain receptors (NLRs) are families of pattern recognition receptors that, together with inflammasomes, sense and respond to highly conserved pathogen motifs and endogenous molecules released upon cell damage or stress. Evidence suggests that TLRs, NLRs and the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome have important roles in kidney diseases through regulation of inflammatory and tissue-repair responses to infection and injury. In this Review, we discuss the pathological mechanisms that are related to TLRs, NLRs and NLRP3 in various kidney diseases. In general, these receptors are protective in the host defence against urinary tract infection, but can sustain and self-perpetuate tissue damage in sterile inflammatory and immune-mediated kidney diseases. TLRs, NLRs and NLRP3, therefore, have become promising drug targets to enable specific modulation of kidney inflammation and suppression of immunopathology in kidney disease.

  13. Orthogonal combination of local binary patterns for dynamic texture recognition

    NASA Astrophysics Data System (ADS)

    Chen, Yin; Guo, Xuejun; Klein, Dominik

    2015-12-01

    Dynamic texture (DT) is an extension of texture to the temporal domain. Recognizing DTs has received increasing attention. Volume local binary pattern (VLBP) is the most widely used descriptor for DTs. However, it is time consuming to recognize DTs using VLBP due to the large scale of data and the high dimensionality of the descriptor itself. In this paper, we propose a new operator called orthogonal combination of VLBP (OC-VLBP) for DT recognition. The original VLBP is decomposed both longitudinally and latitudinally, and then combined to constitute the OC-VLBP operator, so that the dimensionality of the original VLBP descriptor is lowered. The experimental results show that the proposed operator significantly reduces the computational costs of recognizing DTs without much loss in recognizing accuracy.

  14. Pattern recognition analysis of polar clouds during summer and winter

    NASA Technical Reports Server (NTRS)

    Ebert, Elizabeth E.

    1992-01-01

    A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

  15. Using Decision Trees for Comparing Pattern Recognition Feature Sets

    SciTech Connect

    Proctor, D D

    2005-08-18

    Determination of the best set of features has been acknowledged as one of the most difficult tasks in the pattern recognition process. In this report significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. Alternative functional forms for feature sets are also examined. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The method is applied to a problem for which a significant portion of the training set cannot be classified unambiguously.

  16. Innate Immune Pattern Recognition: A Cell Biological Perspective

    PubMed Central

    Brubaker, Sky W.; Bonham, Kevin S.; Zanoni, Ivan

    2016-01-01

    Receptors of the innate immune system detect conserved determinants of microbial and viral origin. Activation of these receptors initiates signaling events that culminate in an effective immune response. Recently, the view that innate immune signaling events rely on and operate within a complex cellular infrastructure has become an important framework for understanding the regulation of innate immunity. Compartmentalization within this infrastructure provides the cell with the ability to assign spatial information to microbial detection and regulate immune responses. Several cell biological processes play a role in the regulation of innate signaling responses; at the same time, innate signaling can engage cellular processes as a form of defense or to promote immunological memory. In this review, we highlight these aspects of cell biology in pattern-recognition receptor signaling by focusing on signals that originate from the cell surface, from endosomal compartments, and from within the cytosol. PMID:25581309

  17. Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    SciTech Connect

    Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

    2010-10-01

    Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

  18. Pattern recognition characterizations of micromechanical and morphological materials states via analytical quantitative ultrasonics

    NASA Technical Reports Server (NTRS)

    Williams, J. H., Jr.; Lee, S. S.

    1986-01-01

    One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

  19. The Recognition Memory Test, Digit Span, and Knox Cube Test as Markers of Malingered Memory Impairment.

    ERIC Educational Resources Information Center

    Iverson, Grant L.; Franzen, Michael D.

    1994-01-01

    Using the Recognition Memory Test, Digit Span subtest of the Wechsler Adult Intelligence Scale-Revised, and the Knox Cube Test as markers for malingered memory deficits was studied with 100 university students, federal inmates, and patients with head injuries. Malingerers performed more poorly than injured patients on all three tests. (SLD)

  20. Studies of the Pattern Recognition Molecule H-ficolin

    PubMed Central

    Zacho, Rikke M.; Jensen, Lisbeth; Terp, Randi; Jensenius, Jens C.; Thiel, Steffen

    2012-01-01

    Ficolins are pattern recognition molecules of the innate immune system. H-ficolin is found in plasma associated with mannan-binding lectin-associated serine proteases (MASPs). When H-ficolin binds to microorganisms the MASPs are activated, which in turn activate the complement system. H-ficolin is the most abundant ficolin in humans, yet its ligand binding characteristics and biological role remain obscure. We examined the binding of H-ficolin to Aerococcus viridans as well as to a more defined artificial target, i.e. acetylated bovine serum albumin. A strict dependence for calcium ions and inhibition at high NaCl concentration was found. The binding to acetylated bovine serum albumin was inhibited by acetylsalicylic acid and sodium acetate as well as by N-acetylated glucosamine and galactosamine (GlcNAc and GalNAc) and glycine (GlyNAc). The binding to A. viridans was sensitive to the same compounds, but, importantly, higher concentrations were needed for inhibition. N-Acetylated cysteine was also inhibitory, but this inhibition was parallel with reduction in the oligomerization of H-ficolin and thus represents structural changes of the molecule. Based on our findings, we developed a procedure for the purification of H-ficolin from serum, involving PEG precipitation, affinity chromatography on Sepharose derivatized with acetylated serum albumin, ion exchange chromatography, and gel permeation chromatography. The purified H-ficolin was observed to elute at 700 kDa, similar to what we find for H-ficolin in whole serum. MASP-2 was co-purified with H-ficolin, and the purified H-ficolin·MASP-2 complex could activate complement as measured by cleavage of complement factor C4. This study extends our knowledge of the specificity of this pattern recognition molecule, and the purified product will enable further studies. PMID:22238349

  1. Recognition of digital characteristics based new improved genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Meng; Xu, Guoqiang; Lin, Zihao

    2017-08-01

    In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.

  2. Proceedings of the Second Annual Symposium on Mathematical Pattern Recognition and Image Analysis Program

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr. (Principal Investigator)

    1984-01-01

    Several papers addressing image analysis and pattern recognition techniques for satellite imagery are presented. Texture classification, image rectification and registration, spatial parameter estimation, and surface fitting are discussed.

  3. Recognition of complex patterned substrates by heteropolymer chains consisting of multiple monomer types.

    PubMed

    Kriksin, Yuri A; Khalatur, Pavel G; Khokhlov, Alexei R

    2006-05-07

    We propose a statistical mechanical model of surface pattern recognition by heteropolymers with quenched monomer sequence distribution. The chemically heterogeneous pattern consists of different adsorption sites specifically distributed on a surface. The heteropolymer sequence is complementary with respect to the pattern. The concepts of recognition probability and recognition temperature are introduced. The algorithm for calculating the recognition probability is based on efficient recurrence procedures for evaluating the single-chain partition function of a chain macromolecule consisting of multiple monomer types, which interact with multiple types of adsorption sites. The temperature dependencies of the recognition probability are discussed. We address the critical role of the commensurability between the heteropolymer sequence and the distribution of the surface adsorbing sites on the polymer adsorption. Also, we address the question of how many types of monomer units in the heteropolymer are required for unambiguous recognition of compact target patterns. It is shown that perfect pattern recognition can be achieved for the strong-adsorption regime in the case of specifically structured compact patterns with multifunctional adsorption sites and heteropolymers with multiple monomer types when the degeneracy of the ground state is suppressed. The pattern recognition ability increases with the number of different types of monomer units and complementary adsorption sites. For random heteropolymers and patterns, the free energy change associated with the recognition process decreases linearly with increasing this number. Correlated random heteropolymers are capable of recognizing related patterns on a random background.

  4. Hydrodynamic model calibration from pattern recognition of non-orthorectified terrestrial photographs

    NASA Astrophysics Data System (ADS)

    Pasquale, N.; Perona, P.; Wombacher, A.; Burlando, P.

    2014-01-01

    This paper presents a remote sensing technique for calibrating hydrodynamics models, which is particularly useful when access to the riverbed for a direct measure of flow variables may be precluded. The proposed technique uses terrestrial photography and automatic pattern recognition analysis together with digital mapping and does not require image ortho-rectification. Compared to others invasive or remote sensing calibration, this method is relatively cheap and can be repeated over time, thus allowing calibration over multiple flow rates . We applied this technique to a sequence of high-resolution photographs of the restored reach of the river Thur, near Niederneunforn, Switzerland. In order to calibrate the roughness coefficient, the actual exposed areas of the gravel bar are first computed using the pattern recognition algorithm, and then compared to the ones obtained from numerical hydrodynamic simulations over the entire range of observed flows. Analysis of the minimum error between the observed and the computed exposed areas show that the optimum roughness coefficient is discharge dependent; particularly it decreases as flow rate increases, as expected. The study is completed with an analysis of the root mean square error (RMSE) and mean absolute error (MEA), which allow finding the best fitting roughness coefficient that can be used over a wide range of flow rates, including large floods.

  5. Image Reconstruction, Recognition, Using Image Processing, Pattern Recognition and the Hough Transform.

    NASA Astrophysics Data System (ADS)

    Seshadri, M. D.

    1992-01-01

    In this dissertation research, we have demonstrated the need for integration of various imaging methodologies, such as image reconstruction from projections, image processing, pattern and feature recognition using chain codes and the Hough transform. Further an integration of these image processing techniques have been brought about for medical imaging systems. An example of this is, classification and identification of brain scans, into normal, haemorrhaged, and lacunar infarcted brain scans. Low level processing was performed using LOG and a variation of LOG. Intermediate level processing used contour completion and chain encoding. Hough transform was used to detect any analytic shapes in the edge images. All these information were used by the data abstraction routine which also extracted information from the user, in the form of a general query. These were input into a backpropagation, which is a very popular supervised neural network. During learning process an output vector was supplied by the expert to the neural network. While performing the neural network compared the input and with the help of the weight matrix computed the output. This output was compared with the expert's opinion and a percentage deviation was calculated. In the case of brain scans this value was about 95%, when the test input vector did not vary, by more than two pixels with the training or learning input vector. A good classification of the brain scans were performed using the integrated imaging system. Identification of various organs in the abdominal region was also successful, within 90% recognition rate, depending on the noise in the image.

  6. Focal-plane CMOS wavelet feature extraction for real-time pattern recognition

    NASA Astrophysics Data System (ADS)

    Olyaei, Ashkan; Genov, Roman

    2005-09-01

    Kernel-based pattern recognition paradigms such as support vector machines (SVM) require computationally intensive feature extraction methods for high-performance real-time object detection in video. The CMOS sensory parallel processor architecture presented here computes delta-sigma (ΔΣ)-modulated Haar wavelet transform on the focal plane in real time. The active pixel array is integrated with a bank of column-parallel first-order incremental oversampling analog-to-digital converters (ADCs). Each ADC performs distributed spatial focal-plane sampling and concurrent weighted average quantization. The architecture is benchmarked in SVM face detection on the MIT CBCL data set. At 90% detection rate, first-level Haar wavelet feature extraction yields a 7.9% reduction in the number of false positives when compared to classification with no feature extraction. The architecture yields 1.4 GMACS simulated computational throughput at SVGA imager resolution at 8-bit output depth.

  7. Pattern recognition system invariant to rotation and scale to identify color images

    NASA Astrophysics Data System (ADS)

    Coronel-Beltrán, Angel

    2014-10-01

    This work presents a pattern recognition digital system based on nonlinear correlations. The correlation peak values given by the system were analyzed by the peak-to-correlation energy (PCE) metric to determine the optimal value of the non-linear coefficient kin the k-law. The system was tested with 18 different color images of butterflies; each image was rotated from 0° to 180° with increments of 1° and scaled ±25% with increments of 1% and to take advantage of the color property of the images the RGB model was employed. The boxplot statistical analysis of the mean with ±2*EE (standard errors) for the PCE values set that the system invariant to rotation and scale has a confidence level at least of 95.4%.

  8. Pattern recognition in geochemical hydrocarbon exploration: a fuzzy approach

    SciTech Connect

    Granath, G.

    1988-08-01

    For the Swedish Deep Gas Project some 240 soil samples were collected and analyzed for trace metals and ..delta.. C. The data were determined to not be sufficient as anomalous patterns obtained were merely reflecting underlying crystalline or Paleozoic bedrock. Any possible patterns related to a deep-seated gas source were completely swamped; in addition glacial transport also presented a problem in interpretation. Therefore, the ARIADNE method was applied to the data set. ARIADNE is a pattern recognition system designed for use in a variety of exploration applications, ranging from geochemical regional surveys to detailed geophysical well logging. The system's core is a fuzzy classifier that can work both on differences in location and dispersion in variable space, either combined or separately. For unsupervised classification, a preprocessor, called NARCISSOS, is used, which, by using fuzzy principal components analysis, extracts a robust background and an appropriate number of anomalous populations. Mean vectors and covariance matrices of all populations are submitted to the ARIADNE classifier. By taking advantage of different patterns emerging by using mean vectors or variance-covariance matrices when classifying in the variable space, the relative influence of transport (e.g., glacial transport) can be estimated and probable source areas also can be established. When ARIADNE was applied to the Deep Gas Project data, two anomalous populations emerged. One was strongly tied, both geographically and chemically, to the Paleozoic ring structure circumscribing the target area, and the background reflected general chemical features of granitic bedrocks inside and outside of that structure. The second anomaly, however, was not related to any bedrock composition, but rather to structural phenomena in the bedrock.

  9. Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms.

    PubMed

    Isasi, A Gola; Zapirain, B García; Zorrilla, A Méndez

    2011-09-01

    In this paper an automated dermatological tool for the parameterization of melanomas is presented. The system is based on the standard ABCD Rule and dermatological Pattern Recognition protocols. On the one hand, a complete stack of algorithms for the asymmetry, border, color, and diameter parameterization were developed. On the other hand, three automatic algorithms for digital image processing have been developed in order to detect the appropriate patterns. These allow one to calculate certain quantitative features based on the aspect and inner patterns of the melanoma using simple-operation algorithms, in order to minimize response time. The database used consists of 160 500 x 500-pixel RGB images (20 images per pattern) cataloged by dermatologists, and the results have turned out to be successful according to assessment by medical experts. While the ABCD algorithms are mathematically reliable, the proposed algorithms for pattern recognition produced a remarkable rate of globular, reticular, and blue veil Pattern recognition, with an average above 85% of accuracy. It thus proves to be a reliable system when performing a diagnosis.

  10. Predictable Pattern Digital Artery Perforator Flap: An Alternative Concept in Digital Reconstruction.

    PubMed

    Epameinondas, Kostopoulos; Christos, Agiannidis; Petros, Konofaos; Avraam, Dounavis; Othon, Papadopoulos; Vincent, Casoli

    2016-03-01

    The proper digital arteries as any other axial vessel give rise to multiple cutaneous perforators either volar or dorsal along their course. Their identification is performed with Doppler flowmetry. The vasculo-cutano-tendino-osseous complex (VCTOC), which was described by the senior authors, was responsible for the vascularization of all digital anatomic structures (extensor apparatus, skin, periosteum). Their consistent appearance to well measured distances from the digital joints led the way to the present clinical study for highlighting this described anatomy in-vivo and demonstrating the predictability in digital artery perforator (DAP) flap harvest. From November 2012 to March 2014, fifteen patients underwent reconstruction with a predictable pattern digital artery perforator flap (PPDAP), based on the previously described VCTOC mapping, for digital lesions secondary to tumor extirpation. Flaps were designed as V-Y advancement or propeller type. Postoperative control concerned flap viability and digital function. Seven males and 8 females underwent elective surgery using PPDAP flaps for digital defects following tumor extirpation. The diameter of the defect ranged from 0.5 to 1.5 cm. The vast majority of the lesions were identified on the right hand, the index, the ring finger and the distal phalanx. All flaps survived without signs of venous congestion. No functional digital problems were observed during follow up (mean of 77 months). A minor wound dehiscence presented in one patient. Authors introduced the concept of a "predictable pattern" in the surgery of perforator flaps in the digits. These flaps are reliable and could be a valuable reconstructive option.

  11. Design Patterns for Digital Item Types in Higher Education

    ERIC Educational Resources Information Center

    Draaijer, S.; Hartog, R. J. M.

    2007-01-01

    A set of design patterns for digital item types has been developed in response to challenges identified in various projects by teachers in higher education. The goal of the projects in question was to design and develop formative and summative tests, and to develop interactive learning material in the form of quizzes. The subject domains involved…

  12. Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures.

    PubMed

    Stoddard, Mary Caswell; Kilner, Rebecca M; Town, Christopher

    2014-06-18

    Pattern-based identity signatures are commonplace in the animal kingdom, but how they are recognized is poorly understood. Here we develop a computer vision tool for analysing visual patterns, NATUREPATTERNMATCH, which breaks new ground by mimicking visual and cognitive processes known to be involved in recognition tasks. We apply this tool to a long-standing question about the evolution of recognizable signatures. The common cuckoo (Cuculus canorus) is a notorious cheat that sneaks its mimetic eggs into nests of other species. Can host birds fight back against cuckoo forgery by evolving highly recognizable signatures? Using NATUREPATTERNMATCH, we show that hosts subjected to the best cuckoo mimicry have evolved the most recognizable egg pattern signatures. Theory predicts that effective pattern signatures should be simultaneously replicable, distinctive and complex. However, our results reveal that recognizable signatures need not incorporate all three of these features. Moreover, different hosts have evolved effective signatures in diverse ways.

  13. Domain architecture evolution of pattern-recognition receptors

    PubMed Central

    Zhang, Qing; Zmasek, Christian M.

    2010-01-01

    In animals, the innate immune system is the first line of defense against invading microorganisms, and the pattern-recognition receptors (PRRs) are the key components of this system, detecting microbial invasion and initiating innate immune defenses. Two families of PRRs, the intracellular NOD-like receptors (NLRs) and the transmembrane Toll-like receptors (TLRs), are of particular interest because of their roles in a number of diseases. Understanding the evolutionary history of these families and their pattern of evolutionary changes may lead to new insights into the functioning of this critical system. We found that the evolution of both NLR and TLR families included massive species-specific expansions and domain shuffling in various lineages, which resulted in the same domain architectures evolving independently within different lineages in a process that fits the definition of parallel evolution. This observation illustrates both the dynamics of the innate immune system and the effects of “combinatorially constrained” evolution, where existence of the limited numbers of functionally relevant domains constrains the choices of domain architectures for new members in the family, resulting in the emergence of independently evolved proteins with identical domain architectures, often mistaken for orthologs. Electronic supplementary material The online version of this article (doi:10.1007/s00251-010-0428-1) contains supplementary material, which is available to authorized users. PMID:20195594

  14. PRoNTo: pattern recognition for neuroimaging toolbox.

    PubMed

    Schrouff, J; Rosa, M J; Rondina, J M; Marquand, A F; Chu, C; Ashburner, J; Phillips, C; Richiardi, J; Mourão-Miranda, J

    2013-07-01

    In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The "Pattern Recognition for Neuroimaging Toolbox" (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.

  15. Network patterns recognition for automatic dermatologic images classification

    NASA Astrophysics Data System (ADS)

    Grana, Costantino; Daniele, Vanini; Pellacani, Giovanni; Seidenari, Stefania; Cucchiara, Rita

    2007-03-01

    In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is performed with a three-steps procedure. The first step is the localization of line points, by means of the line points detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering the direction of the line at its endpoints and the number of line points connected to these. Finally a third step discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular. This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular lesions on real skin images, with a specificity value never lower than 92%.

  16. Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters.

    PubMed

    Poularakis, Stergios; Katsavounidis, Ioannis

    2016-09-01

    In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.

  17. Hybrid Optical/Digital Architecture for Distortion Invariant Pattern Recognition

    DTIC Science & Technology

    1989-12-01

    be segmented using any number of segmentation techniques, including the Gabor transform [24], or techniques employed by Mike Roggemann [25]. The...September 1989. 24. Ayer, Capt Kevin, Gabor Transforms for Forward Looking Infrared Image Segmentation. MS thesis, AFIT/GEO/ENG/89D-1. School of...Ruck, Capt Dennis , PhD student. Personal interviews. Air Force Institute of Technology, Wright-Patterson AFB OH, November 1989. 36. "Spiricon 2250

  18. A Novel Optical/digital Processing System for Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Boone, Bradley G.; Shukla, Oodaye B.

    1993-01-01

    This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network.

  19. Spectral and Spatial Pattern Recognition in Digital Imagery

    DTIC Science & Technology

    1992-12-01

    soil axis used in the PVI equations is based on 16 data points in Texas, from four dates in 1975. "It is unlikely that such a small and geographically ...This process requires that multiple images of a specific geographic region be taken in several spectral bands and that the images be in good...used to image the geographic region. For ease of explanation and understanding, only two imaging bands will be used in the discussion on supervised

  20. Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

    NASA Astrophysics Data System (ADS)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

  1. A Comparative Study of Random Patterns for Digital Image Correlation

    NASA Astrophysics Data System (ADS)

    Stoilov, G.; Kavardzhikov, V.; Pashkouleva, D.

    2012-06-01

    Digital Image Correlation (DIC) is a computer based image analysis technique utilizing random patterns, which finds applications in experimental mechanics of solids and structures. In this paper a comparative study of three simulated random patterns is done. One of them is generated according to a new algorithm, introduced by the authors. A criterion for quantitative evaluation of random patterns after the calculation of their autocorrelation functions is introduced. The patterns' deformations are simulated numerically and realized experimentally. The displacements are measured by using the DIC method. Tensile tests are performed after printing the generated random patterns on surfaces of standard iron sheet specimens. It is found that the new designed random pattern keeps relatively good quality until reaching 20% deformation.

  2. Digitally based pattern generator for an electron-beam welder

    SciTech Connect

    Whitten, L.G. III

    1981-02-19

    A digitally based deflection generator for an electron-beam welder is presented. Up to seven patterns of any shape are stored in programmable read-only memory (PROM). The pattern resolution is 39% at frequencies from 10 Hz to 1 kHz and can be x-t, y-t, or x-y formed. Frequency and pattern selections may be chosen by the welder computer or manually selected on the front panel. The ability to repeatedly synchronize two waveforms of any shape and frequency enables an unlimited variety of welds.

  3. Dioxin screening in fish product by pattern recognition of biomarkers.

    PubMed

    Bassompierre, Marc; Tomasi, Giorgio; Munck, Lars; Bro, Rasmus; Engelsen, Søren Balling

    2007-04-01

    Two alternative, cost- and time-effective dioxin screening methods relying on two categories of potential lipid biomarkers were investigated. A dioxin range varying from 1.1 to 47.1 pg PCDD/F TEQ-WHO/g lipid using 64 fish meal samples was used for model calibration. The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pgPCDD/F TEQ-WHO/g lipid. In both cases, Partial Least Squares (PLS) regression was performed for predicting the dioxin content of a sample. The GC-FID data analyses was based on automatic peak alignment and integration, enabling extraction of the area of 140 peaks from the gas chromatograms, as opposed to the 31 fatty acids usually considered for fish oil characterisation. In addition to classic PLS employing the whole dataset for calibration, a two-step local PLS modeling approach was performed based upon an initial selection of k number of calibration samples providing the best match to the prediction sample using a so-called k Nearest Neighbors (kNN) approach, then followed by PLS calibration on these kNN selected samples for dioxin prediction. Fluorescence spectroscopy offers a promising non-invasive and ultra-rapid technique, with less than two minutes analysis time. However, fluorescence spectroscopy using the pattern recognition "kNN-PLS" yielded a correlation of 0.76 (r2) and a high root mean square error of prediction of 11.4 pg PCDD/F TEQ-WHO/g lipid. The predictions were improved when the PLS calibration was performed on all the sample with a root mean square error of prediction of 7.0 pg PCDD/F TEQ-WHO/g lipid. Unfortunately, these results failed to demonstrate the potential of fluorophore monitoring as a screening method. In contrast, the overall best screening performance was obtained with the fatty acid profile, when the kNN-PLS combination employed for pattern

  4. Interactions of age, ear, and stimulus complexity on dichotic digit recognition.

    PubMed

    Wilson, R H; Jaffe, M S

    1996-10-01

    The effect that the aging process has on recognition performance was studied using a hierarchy of 1-pair, 2-pair, 3-pair, and 4-pair dichotic digits (1, 2, 3, 4, 5, 6, 8, 9, and 10, male speaker). Two groups of right-handed subjects were studied: 20 adults < 30 years of age with normal hearing and 20 adults 60-75 years with mild-to-moderate hearing loss. Each of the multipaired sets contained the 72 possible 1-pair digit combinations in each presentation position with no digits repeated in a set. A three-way analysis of variance with repeated measures indicated three significant differences. First, as the complexity of the listening task increased from 1 pair to 2 pairs to 3 pairs to 4 pairs, recognition performance decreased systematically and significantly. The decreases in performance between the 1- and 4-paired conditions were larger for the left ear than for the right ear and were larger for the 60-75 years group than for the < 30 years group. Second, performance on the materials presented to the right ear was significantly better than performance on the materials presented to the left ear. Third, recognition performance by the < 30 years group was significantly better than recognition performance by the 60-75 years group. Recognition performance on the 1-pair condition was near maximum for both subject groups. Between the 1- and 4-paired conditions, recognition performance for materials presented to the left and right ears decreased 15.7 percent and 10.0 percent, respectively, in the < 30 years group and decreased 29.3 percent and 15.7 percent, respectively, in the 60-75 years group.

  5. Parallel optical Walsh expansion in a pattern recognition preprocessor using planar microlens array

    NASA Astrophysics Data System (ADS)

    Murashige, Kimio; Akiba, Atsushi; Baba, Toshihiko; Iga, Kenichi

    1992-05-01

    A parallel optical processor developed for a pattern recognition system using a planar microlens array and a Walsh orthogonal expansion spatial filter is developed. The parallel optical Walsh expansion of multiple images made by the planar microlens array with good accuracy, which assures 99-percent recognition of simple numeral characters in the system, is demonstrated. A novel selection method of Walsh expansion coefficients is proposed in order to enlarge the tolerance of the recognition rate against the deformation of input patterns.

  6. Workshop on Standards for Image Pattern Recognition. Computer Seience & Technology Series.

    ERIC Educational Resources Information Center

    Evans, John M. , Ed.; And Others

    Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications. Automatic Image Pattern Recognition Algorithms are often developed and tested using unique data bases for each specific application. Quantitative comparison of different approaches and…

  7. Workshop on Standards for Image Pattern Recognition. Computer Seience & Technology Series.

    ERIC Educational Resources Information Center

    Evans, John M. , Ed.; And Others

    Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications. Automatic Image Pattern Recognition Algorithms are often developed and tested using unique data bases for each specific application. Quantitative comparison of different approaches and…

  8. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data

    SciTech Connect

    Derde, M.P.; Buydens, L.; Guns, C.; Massart, D.L.; Hopke, P.K.

    1987-07-15

    Two expert systems of the rule-building type, TIMM and EX-TRAN, are compared with pattern recognition methods for the classification of olive oils of different origins. Both expert systems are more user-friendly than the pattern recognition programs and TIMM yields slightly better results than nearest neighbors classifiers and linear discriminant analysis.

  9. Pattern recognition and PID procedure with the ALICE-HMPID

    NASA Astrophysics Data System (ADS)

    Volpe, Giacomo

    2014-12-01

    The ALICE apparatus is dedicated to the study of pp, p-Pb and Pb-Pb collisions provided by LHC. ALICE has unique particle identification (PID) capabilities among the LHC experiments exploiting different PID techniques, i.e., energy loss, time-of-flight measurements, Cherenkov and transition radiation detection, calorimetry and topological ID. The ALICE-HMPID is devoted to the identification of charged hadrons. It consists of seven identical RICH counters, with liquid C6F14 as Cherenkov radiator (n≈1.299 at λph=175 nm). Photons and charged particles detection is performed by a proportional chamber, coupled with a pad segmented CsI coated photo-cathode. In pp and p-Pb events HMPID provides 3 sigmas separation for pions and kaons up to pT = 3 GeV / c and for protons up to pT = 5 GeV / c. PID is performed by means of photon emission angle measurement, a challenging task in the high multiplicity environment of the most central Pb-Pb collisions. A dedicated algorithm has been implemented to evaluate the Cherenkov angle starting from the bi-dimensional ring pattern on the photons detector, it is based on the Hough Transform Method (HTM) to separate signal from background. In this way HMPID is able to contribute to inclusive hadrons spectra measurement as well as to measurements where high purity PID is required, by means of statistical or track-by-track PID. The pattern recognition, the results from angular resolution studies and the PID strategy with HMPID are presented.

  10. The digits-in-noise test: assessing auditory speech recognition abilities in noise.

    PubMed

    Smits, Cas; Theo Goverts, S; Festen, Joost M

    2013-03-01

    A speech-in-noise test which uses digit triplets in steady-state speech noise was developed. The test measures primarily the auditory, or bottom-up, speech recognition abilities in noise. Digit triplets were formed by concatenating single digits spoken by a male speaker. Level corrections were made to individual digits to create a set of homogeneous digit triplets with steep speech recognition functions. The test measures the speech reception threshold (SRT) in long-term average speech-spectrum noise via a 1-up, 1-down adaptive procedure with a measurement error of 0.7 dB. One training list is needed for naive listeners. No further learning effects were observed in 24 subsequent SRT measurements. The test was validated by comparing results on the test with results on the standard sentences-in-noise test. To avoid the confounding of hearing loss, age, and linguistic skills, these measurements were performed in normal-hearing subjects with simulated hearing loss. The signals were spectrally smeared and/or low-pass filtered at varying cutoff frequencies. After correction for measurement error the correlation coefficient between SRTs measured with both tests equaled 0.96. Finally, the feasibility of the test was approved in a study where reference SRT values were gathered in a representative set of 1386 listeners over 60 years of age.

  11. Scalable pattern recognition for large-scale scientific data mining

    SciTech Connect

    Kamath, C.; Musick, R.

    1998-03-23

    Our ability to generate data far outstrips our ability to explore and understand it. The true value of this data lies not in its final size or complexity, but rather in our ability to exploit the data to achieve scientific goals. The data generated by programs such as ASCI have such a large scale that it is impractical to manually analyze, explore, and understand it. As a result, useful information is overlooked, and the potential benefits of increased computational and data gathering capabilities are only partially realized. The difficulties that will be faced by ASCI applications in the near future are foreshadowed by the challenges currently facing astrophysicists in making full use of the data they have collected over the years. For example, among other difficulties, astrophysicists have expressed concern that the sheer size of their data restricts them to looking at very small, narrow portions at any one time. This narrow focus has resulted in the loss of ``serendipitous`` discoveries which have been so vital to progress in the area in the past. To solve this problem, a new generation of computational tools and techniques is needed to help automate the exploration and management of large scientific data. This whitepaper proposes applying and extending ideas from the area of data mining, in particular pattern recognition, to improve the way in which scientists interact with large, multi-dimensional, time-varying data.

  12. Infrared target simulation environment for pattern recognition applications

    NASA Astrophysics Data System (ADS)

    Savakis, Andreas E.; George, Nicholas

    1994-07-01

    The generation of complete databases of IR data is extremely useful for training human observers and testing automatic pattern recognition algorithms. Field data may be used for realism, but require expensive and time-consuming procedures. IR scene simulation methods have emerged as a more economical and efficient alternative for the generation of IR databases. A novel approach to IR target simulation is presented in this paper. Model vehicles at 1:24 scale are used for the simulation of real targets. The temperature profile of the model vehicles is controlled using resistive circuits which are embedded inside the models. The IR target is recorded using an Inframetrics dual channel IR camera system. Using computer processing we place the recorded IR target in a prerecorded background. The advantages of this approach are: (1) the range and 3D target aspect can be controlled by the relative position between the camera and model vehicle; (2) the temperature profile can be controlled by adjusting the power delivered to the resistive circuit; (3) the IR sensor effects are directly incorporated in the recording process, because the real sensor is used; (4) the recorded target can embedded in various types of backgrounds recorded under different weather conditions, times of day etc. The effectiveness of this approach is demonstrated by generating an IR database of three vehicles which is used to train a back propagation neural network. The neural network is capable of classifying vehicle type, vehicle aspect, and relative temperature with a high degree of accuracy.

  13. Spectral-spatial classification to pattern recognition of hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Su, Tung-Ching

    2012-04-01

    Recently several spectral-spatial classification methods had been presented and applied to pattern recognition of hyperspectral imagery. However, the present methods are especially suitable for classifying images with large spatial structures in spite of the derived classification accuracies of above 90%. To classify hyperspectral images with larger as well as smaller spatial structures, a novel spectral-spatial classification method was presented and tested on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image with 145×145 pixels and 220 bands. Firstly, the AVIRIS image was implemented a spectral mixture analysis using minimum noise fraction (MNF). Based on the obtained n-dimensional eigenimage, support vector machine (SVM) was used to classify the AVIRIS image. Simultaneously, the eigenimage was calculated the mathematical morphology-based image gradients for the n dimensions so to obtain n watershed segmentation images. Finally, the SVM classification map was turned into several new ones through a series of post-processing. The experimental results verify that the proposed spectral-spatial classification method has the capability to detect larger as well as smaller spatial structures in hyperspectral imagery.

  14. Shape connection by pattern recognition and laser metrology.

    PubMed

    Muñoz-Rodríguez, J Apolinar

    2008-07-10

    Shape connection based on the pattern recognition of three-dimensional shapes is presented. In this technique, the object shape is reconstructed by laser scanning and image processing. The object is reconstructed from multiple views when an object occlusion appears. From this process, multiple parts of the object are reconstructed. Then, these parts are assembled to obtain the complete object shape. To perform the assembling, a matching procedure is applied to a transverse section of the multiple views by Hu moments. The depth of the transverse section is computed by an approximation network based on the behavior of the laser line and the camera position. Also, vision parameters are deduced by the network and image processing. In this manner, the shape connection is achieved automatically by computational algorithms. Therefore, errors of physical measurement are not passed to the reconstruction system. Thus, the performance and the accuracy of the reconstruction system are improved. This is elucidated by the comparison between the obtained results by the proposed technique and the obtained results by a contact method. Thus, a contribution in laser metrology for shape connection is achieved.

  15. A natural approach to convey numerical digits using hand activity recognition based on hand shape features

    NASA Astrophysics Data System (ADS)

    Chidananda, H.; Reddy, T. Hanumantha

    2017-06-01

    This paper presents a natural representation of numerical digit(s) using hand activity analysis based on number of fingers out stretched for each numerical digit in sequence extracted from a video. The analysis is based on determining a set of six features from a hand image. The most important features used from each frame in a video are the first fingertip from top, palm-line, palm-center, valley points between the fingers exists above the palm-line. Using this work user can convey any number of numerical digits using right or left or both the hands naturally in a video. Each numerical digit ranges from 0 to9. Hands (right/left/both) used to convey digits can be recognized accurately using the valley points and with this recognition whether the user is a right / left handed person in practice can be analyzed. In this work, first the hand(s) and face parts are detected by using YCbCr color space and face part is removed by using ellipse based method. Then, the hand(s) are analyzed to recognize the activity that represents a series of numerical digits in a video. This work uses pixel continuity algorithm using 2D coordinate geometry system and does not use regular use of calculus, contours, convex hull and datasets.

  16. Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography.

    PubMed

    Moon, Inkyu; Javidi, Bahram

    2006-01-01

    We present a new statistical approach to real-time sensing and recognition of microorganisms using digital holographic microscopy. We numerically produce many section images at different depths along a longitudinal direction from the single digital hologram of three-dimensional (3D) microorganisms in the Fresnel domain. For volumetric 3D recognition, the test pixel points are randomly selected from the section image; this procedure can be repeated with different specimens of the same microorganism. The multivariate joint density functions are calculated from the pixel values of each section image at the same random pixel points. The parameters of the statistical distributions are compared using maximum likelihood estimation and statistical inference algorithms. The performance of the proposed system is illustrated with preliminary experimental results.

  17. Pattern Recognition: The Importance of Dispersion in Crystal Collimation

    SciTech Connect

    Peggs,S.; Shiraishi, S.

    2008-09-01

    One aspect of the upcoming CRYSTAL experiment is to study the dynamics of single protons circulating the SPS in the presence of a crystal. Under some circumstances (for example under crystal channeling) a proton may hit the crystal and the neighboring silicon strip position detectors only once, before extraction from the SPS. In general (at most crystal rotation angles) it is expected that single protons will hit the crystal many times, with many accelerator turns between each hit, before escaping. Intermediate regimes are also possible (for example under volume reflection) in which a proton hits the crystal only a few times over many turns before being lost. It is crucial that the data analysis of each single proton data set be able to distinguish between these different dynamical phases, and to be able to convincingly demonstrate that the fundamental processes at play in each phase are well understood. Distinguishing between dynamical phases depends crucially on the ability to perform pattern recognition--at least visually, but preferably quantitatively--on the single proton data sets. This note shows that synchrotron oscillations significantly affect the hit pattern of a proton on the crystal. (By hit pattern we mean either the measurement vector of turn number and penetration depth, for each proton, or a vector that can be directly derived from the measurement vector, such as the vector of inferred synchrotron phase and penetration depth.) The analysis is (deliberately) as rudimentary as possible, using an elementary linear calculation which neither includes any higher order effects in the accelerator, nor any dynamical interactions between the test proton and the crystal or the silicon detectors. Single particle simulation studies need to be carried out for CRYSTAL, exploring realistic effects besides dispersion, such as multiple scattering, dead zones, energy loss, dispersion slope, and linear coupling. Only after analysis software becomes available to

  18. Patterning and post-patterning modes of evolutionary digit loss in mammals.

    PubMed

    Cooper, Kimberly L; Sears, Karen E; Uygur, Aysu; Maier, Jennifer; Baczkowski, Karl-Stephan; Brosnahan, Margaret; Antczak, Doug; Skidmore, Julian A; Tabin, Clifford J

    2014-07-03

    A reduction in the number of digits has evolved many times in tetrapods, particularly in cursorial mammals that travel over deserts and plains, yet the underlying developmental mechanisms have remained elusive. Here we show that digit loss can occur both during early limb patterning and at later post-patterning stages of chondrogenesis. In the 'odd-toed' jerboa (Dipus sagitta) and horse and the 'even-toed' camel, extensive cell death sculpts the tissue around the remaining toes. In contrast, digit loss in the pig is orchestrated by earlier limb patterning mechanisms including downregulation of Ptch1 expression but no increase in cell death. Together these data demonstrate remarkable plasticity in the mechanisms of vertebrate limb evolution and shed light on the complexity of morphological convergence, particularly within the artiodactyl lineage.

  19. Denoising in digital speckle pattern interferometry using wave atoms.

    PubMed

    Federico, Alejandro; Kaufmann, Guillermo H

    2007-05-15

    We present an effective method for speckle noise removal in digital speckle pattern interferometry, which is based on a wave-atom thresholding technique. Wave atoms are a variant of 2D wavelet packets with a parabolic scaling relation and improve the sparse representation of fringe patterns when compared with traditional expansions. The performance of the denoising method is analyzed by using computer-simulated fringes, and the results are compared with those produced by wavelet and curvelet thresholding techniques. An application of the proposed method to reduce speckle noise in experimental data is also presented.

  20. Double-pulse digital speckle pattern interferometry for vibration analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Dazhi; Xue, Jingfeng; Chen, Lu; Wen, Juying; Wang, Jingjing

    2014-12-01

    The double-pulse Digital Speckle Pattern Interferometry (DSPI) in the laboratory is established. Two good performances have been achieved at the same time, which is uniform distribution of laser beam energy by space filter and recording two successive pictures by a CCD camera successfully. Then two-dimensional discrete orthogonal wavelet transform method is used for the process of filtering method. By using the DSPI, speckle pattern of a vibrated object is obtained with interval of (2~800)μs, and 3D plot of the transient vibration is achieved. Moreover, good agreements of the mode shapes and displacement are obtained by comparing with Laser Doppler Vibrometer (LDV) .

  1. Target recognition and phase acquisition by using incoherent digital holographic imaging

    NASA Astrophysics Data System (ADS)

    Lee, Munseob; Lee, Byung-Tak

    2017-05-01

    In this study, we proposed the Incoherent Digital Holographic Imaging (IDHI) for recognition and phase information of dedicated target. Although recent development of a number of target recognition techniques such as LIDAR, there have limited success in target discrimination, in part due to low-resolution, low scanning speed, and computation power. In the paper, the proposed system consists of the incoherent light source, such as LED, Michelson interferometer, and digital CCD for acquisition of four phase shifting image. First of all, to compare with relative coherence, we used a source as laser and LED, respectively. Through numerical reconstruction by using the four phase shifting method and Fresnel diffraction method, we recovered the intensity and phase image of USAF resolution target apart from about 1.0m distance. In this experiment, we show 1.2 times improvement in resolution compared to conventional imaging. Finally, to confirm the recognition result of camouflaged targets with the same color from background, we carry out to test holographic imaging in incoherent light. In this result, we showed the possibility of a target detection and recognition that used three dimensional shape and size signatures, numerical distance from phase information of obtained holographic image.

  2. The effects of digital signal processing features on children's speech recognition and loudness perception.

    PubMed

    Crukley, Jeffery; Scollie, Susan D

    2014-03-01

    The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

  3. Fundamental remote science research program. Part 2: Status report of the mathematical pattern recognition and image analysis project

    NASA Technical Reports Server (NTRS)

    Heydorn, R. P.

    1984-01-01

    The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of he Earth from remotely sensed measurements of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inferences about the Earth. This report summarizes the progress that has been made toward this program goal by each of the principal investigators in the MPRIA Program.

  4. Applications of pattern recognition techniques to online fault detection

    SciTech Connect

    Singer, R.M.; Gross, K.C.; King, R.W.

    1993-11-01

    A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator`s response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented.

  5. Multiresolution pattern recognition of small volcanos in Magellan data

    NASA Technical Reports Server (NTRS)

    Smyth, P.; Anderson, C. H.; Aubele, J. C.; Crumpler, L. S.

    1992-01-01

    The Magellan data is a treasure-trove for scientific analysis of venusian geology, providing far more detail than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations. However, at this point, planetary scientists are being overwhelmed by the sheer quantities of data collected--data analysis technology has not kept pace with our ability to collect and store it. In particular, 'small-shield' volcanos (less than 20 km in diameter) are the most abundant visible geologic feature on the planet. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanos visible in the Magellan data. Identifying and studying these volcanos is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is very time-consuming. Hence, we have undertaken the development of techniques to partially automate this task. The goal is not the unrealistic one of total automation, but rather the development of a useful tool to aid the project scientists. The primary constraints for this particular problem are as follows: (1) the method must be reasonably robust; and (2) the method must be reasonably fast. Unlike most geological features, the small volcanos of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include the following: (1) a circular planimetric outline; (2) known diameter frequency distribution from preliminary studies; (3) a limited number of basic morphological shapes; and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

  6. Pattern Recognition in Optical Remote Sensing Data Processing

    NASA Astrophysics Data System (ADS)

    Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

    Computational procedures of the land surface biophysical parameters retrieval imply that modeling techniques are available of the outgoing radiation description together with monitoring techniques of remote sensing data processing using registered radiances between the related optical sensors and the land surface objects called “patterns”. Pattern recognition techniques are a valuable approach to the processing of remote sensing data for images of the land surface - atmosphere system. Many simplified codes of the direct and inverse problems of atmospheric optics are considered applicable for the imagery processing of low and middle spatial resolution. Unless the authors are not interested in the accuracy of the final information products, they utilize these standard procedures. The emerging necessity of processing data of high spectral and spatial resolution given by imaging spectrometers puts forward the newly defined pattern recognition techniques. The proposed tools of using different types of classifiers combined with the parameter retrieval procedures for the forested environment are maintained to have much wider applications as compared with the image features and object shapes extraction, which relates to photometry and geometry in pixel-level reflectance representation of the forested land cover. The pixel fraction and reflectance of “end-members” (sunlit forest canopy, sunlit background and shaded background for a particular view and solar illumination angle) are only a part in the listed techniques. It is assumed that each pixel views collections of the individual forest trees and the pixel-level reflectance can thus be computed as a linear mixture of sunlit tree tops, sunlit background (or understory) and shadows. Instead of these photometry and geometry constraints, the improved models are developed of the functional description of outgoing spectral radiation, in which such parameters of the forest canopy like the vegetation biomass density for

  7. Effect of spectral resolution on pattern recognition analysis using passive fourier transform infrared sensor data

    SciTech Connect

    Bangalore, Arjun S.; Demirgian, Jack C.; Boparai, Amrit S.; Small, Gary W.

    1999-11-01

    The Fourier transform infrared (FT-IR) spectral data of two nerve agent simulants, diisopropyl methyl phosphonate (DIMP) and dimethyl methyl phosphonate (DMMP), are used as test cases to determine the spectral resolution that gives optimal pattern recognition performance. DIMP is used as the target analyte for detection, while DMMP is used to test the ability of the automated pattern recognition methodology to detect the analyte selectively. Interferogram data are collected by using a Midac passive FT-IR instrument. The methodology is based on the application of pattern recognition techniques to short segments of single-beam spectra obtained by Fourier processing the collected interferogram data. The work described in this article evaluates the effect of varying spectral resolution on the pattern recognition results. The objective is to determine the optimal spectral resolution to be used for data collection. The results of this study indicate that the data with a nominal spectral resolution of 16 cm{sup -1} provide sufficient selectivity to give pattern recognition results comparable to that obtained by using higher resolution data. We found that, while higher resolution does not increase selectivity sufficiently to provide better pattern recognition results, lower resolution decreases selectivity and degrades the pattern recognition results. These results can be used as guidelines to maximize detection sensitivity, to minimize the time needed for data collection, and to reduce data storage requirements. (c) 2000 Society for Applied Spectroscopy.

  8. Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Baochang; Wang, Zongli; Zhong, Bineng

    2008-12-01

    This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

  9. On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information

    NASA Astrophysics Data System (ADS)

    Stathopoulou, I.-O.; Alepis, E.; Tsihrintzis, G. A.; Virvou, M.

    Towards realizing a multimodal affect recognition system, we are considering the advantages of assisting a visual-facial expression recognition system with keyboard-stroke pattern information. Our work is based on the assumption that the visual-facial and keyboard modalities are complementary to each other and that their combination can significantly improve the accuracy in affective user models. Specifically, we present and discuss the development and evaluation process of two corresponding affect recognition subsystems, with emphasis on the recognition of 6 basic emotional states, namely happiness, sadness, surprise, anger and disgust as well as the emotion-less state which we refer to as neutral. We find that emotion recognition by the visual-facial modality can be aided greatly by keyboard-stroke pattern information and the combination of the two modalities can lead to better results towards building a multimodal affect recognition system.

  10. The software peculiarities of pattern recognition in track detectors

    SciTech Connect

    Starkov, N.

    2015-12-31

    The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.

  11. Galectins as Pattern Recognition Receptors: Structure, Function, and Evolution

    PubMed Central

    Vasta, Gerardo R.

    2012-01-01

    Galectins constitute an evolutionary conserved family of β-galactoside-binding proteins, ubiquitous in mammals and other vertebrate taxa, invertebrates, and fungi. Since their discovery in the 1970s, their biological roles, initially understood as limited to recognition of carbohydrate ligands in embryogenesis and development, have expanded in recent years by the discovery of their immunoregulatory activities. A gradual paradigm shift has taken place in the past few years through the recognition that galectins also bind glycans on the surface of potentially pathogenic microbes, and function as recognition and effector factors in innate immunity. Further, an additional level of functional complexity has emerged with the most recent findings that some parasites “subvert” the recognition roles of the vector/host galectins for successful attachment or invasion. PMID:21948360

  12. Test patterns and quality metrics for digital video compression

    NASA Astrophysics Data System (ADS)

    Fenimore, Charles P.; Field, Bruce F.; Van Degrift, Craig

    1997-06-01

    Lossy video compression systems such as MPEG2 introduce picture impairments such as image blocking, color distortion and persistent color fragments, 'mosquito noise,' and blurring in their outputs. While there are video test clips which exhibit one or more of these distortions upon coding, there is need of a set of well-characterized test patterns and video quality metrics. Digital test patterns can deliver calibrated stresses to specific features of the encoder, much as the test patterns for analog video stress critical characteristics of that system. Metrics quantify the error effects of compression by a computation. NIST is developing such test patterns and metrics for compression rates that typically introduce perceptually negligible artifacts, i.e. for high quality video. The test patterns are designed for subjective and objective evaluation. The test patterns include a family of computer-generated spinning wheels to stress luminance-based macro-block motion estimation algorithms and images with strongly directional high-frequency content to stress quantization algorithms. In this paper we discuss the spinning wheel test pattern. It has been encoded at a variety of bit rates near the threshold for the perception of impairments. We have observed that impairment perceptibility depends on the local contrast. For the spinning wheel we report the contrast at the threshold for perception of impairments as a function of the bit rate. To quantify perceptual image blocking we have developed a metric which detects 'flats:' image blocks of constant (or near constant) luminance. The effectiveness of this metric is appraised.

  13. The role of binocular disparity in rapid scene and pattern recognition

    PubMed Central

    Valsecchi, Matteo; Caziot, Baptiste; Backus, Benjamin T.; Gegenfurtner, Karl R.

    2013-01-01

    We investigated the contribution of binocular disparity to the rapid recognition of scenes and simpler spatial patterns using a paradigm combining backward masked stimulus presentation and short-term match-to-sample recognition. First, we showed that binocular disparity did not contribute significantly to the recognition of briefly presented natural and artificial scenes, even when the availability of monocular cues was reduced. Subsequently, using dense random dot stereograms as stimuli, we showed that observers were in principle able to extract spatial patterns defined only by disparity under brief, masked presentations. Comparing our results with the predictions from a cue-summation model, we showed that combining disparity with luminance did not per se disrupt the processing of disparity. Our results suggest that the rapid recognition of scenes is mediated mostly by a monocular comparison of the images, although we can rely on stereo in fast pattern recognition. PMID:23755357

  14. Real-time decomposition and recognition of acoustical patterns with an analog neural computer

    NASA Astrophysics Data System (ADS)

    Mueller, Paul; Van der Spiegel, Jan; Blackman, David; Donham, Christopher; Cummings, Ralph

    1992-09-01

    A prototype programmable analog neural computer has been assembled from over 100 custom VLSI modules containing neurons, synapses, routing switches, and programmable synaptic time constants. The modules are directly interconnected and arbitrary network configurations can be programmed. Connection symmetry and modular construction allow expansion of the network to any size. The network runs in real time analog mode, but connection architecture as well as neuron and synapse parameters are controlled by a digital host. Network performance is monitored by the host through an A/D interface and used in the implementation of learning algorithms. The machine is intended for real time, real world computations. In its current configuration maximal speed is equivalent to that of a digital machine capable of 1011 FLOPS. The programmable synaptic time constants permit the real time computation of temporal patterns as they occur in speech and other acoustic signals. Several applications involving the dynamic decomposition and recognition of acoustical patterns including speech signals (phonemes) are described. The decomposition network is loosely based on the primary auditory system of higher vertebrates. It extracts and represents by the activity in different neuron arrays the following pattern primitives: frequency, bandwidth, amplitude, amplitude modulation, amplitude modulation frequency, frequency modulation, frequency modulation frequency, duration, sequence. The frequency tuned units are the first stage and form the input space for subsequent stages that extract the other primitives, e.g., bandwidth, amplitude modulation, etc., for different frequency bands. Acoustic input generates highly specific, relatively sparse distributed activity in this feature space, which is decoded and recognized by units trained by specific input patterns such as phonemes or diphones or active sonar patterns. Through simple feedback connections in conjunction with synaptic time constants the

  15. Single nucleotide polymorphisms of pattern recognition receptors and chronic periodontitis.

    PubMed

    Sahingur, S E; Xia, X-J; Gunsolley, J; Schenkein, H A; Genco, R J; De Nardin, E

    2011-04-01

    Periodontitis is a multifactorial disease influenced partly by genetics. Activation of pattern recognition receptors (PRRs) can lead to the up-regulation of inflammatory pathways, resulting in periodontal tissue destruction. Hence, functional polymorphisms located in PRRs can explain differences in host susceptibility to periodontitis. This study investigated single nucleotide polymorphisms of PRRs including toll-like receptor (TLR)2 (G2408A), TLR4 (A896G), TLR9 (T1486C), TLR9 (T1237C) and CD14 (C260T) in patients with chronic periodontitis and in periodontally healthy subjects. One-hundred and fourteen patients with chronic periodontitis and 77 periodontally healthy subjects were genotyped using TaqMan® allelic discrimination assays. Fisher's exact test and chi-square analyses were performed to compare genotype and allele frequencies. The frequency of subjects with the CC genotype of CD14 (C260T) (24.6% in the chronic periodontitis group vs. 13% in the periodontally healthy group) and those expressing the T allele of CD14 (C260T) (CT and TT) (75.4% in the chronic periodontitis group vs. 87% in the periodontally healthy group) was statistically different among groups (p = 0.04). Homozygocity for the C allele of the CD14 (C260T) polymorphism (CC) was associated with a two--fold increased susceptibility to periodontitis (p = 0.04; odds ratio, 2.49; 95% confidence interval, 1.06-6.26). Individuals with the CC genotype of TLR9 (T1486C) (14.9% in the chronic periodontitis group vs. 28.6% in the periodontally healthy group) and those expressing the T allele of TLR9 (T1486C) (CT and TT) (85.1% in the chronic periodontitis group vs. 71.4% in the periodontally healthy group) were also significantly differently distributed between groups without adjustment (p = 0.03). Further analysis of nonsmokers revealed a significant difference in the distribution of genotypes between groups for TLR9 (T1486C; p = 0.017) and CD14 (C260T; p = 0.03), polymorphisms again without adjustment

  16. Songbirds use spectral shape, not pitch, for sound pattern recognition.

    PubMed

    Bregman, Micah R; Patel, Aniruddh D; Gentner, Timothy Q

    2016-02-09

    Humans easily recognize "transposed" musical melodies shifted up or down in log frequency. Surprisingly, songbirds seem to lack this capacity, although they can learn to recognize human melodies and use complex acoustic sequences for communication. Decades of research have led to the widespread belief that songbirds, unlike humans, are strongly biased to use absolute pitch (AP) in melody recognition. This work relies almost exclusively on acoustically simple stimuli that may belie sensitivities to more complex spectral features. Here, we investigate melody recognition in a species of songbird, the European Starling (Sturnus vulgaris), using tone sequences that vary in both pitch and timbre. We find that small manipulations altering either pitch or timbre independently can drive melody recognition to chance, suggesting that both percepts are poor descriptors of the perceptual cues used by birds for this task. Instead we show that melody recognition can generalize even in the absence of pitch, as long as the spectral shapes of the constituent tones are preserved. These results challenge conventional views regarding the use of pitch cues in nonhuman auditory sequence recognition.

  17. Songbirds use spectral shape, not pitch, for sound pattern recognition

    PubMed Central

    Bregman, Micah R.; Patel, Aniruddh D.; Gentner, Timothy Q.

    2016-01-01

    Humans easily recognize “transposed” musical melodies shifted up or down in log frequency. Surprisingly, songbirds seem to lack this capacity, although they can learn to recognize human melodies and use complex acoustic sequences for communication. Decades of research have led to the widespread belief that songbirds, unlike humans, are strongly biased to use absolute pitch (AP) in melody recognition. This work relies almost exclusively on acoustically simple stimuli that may belie sensitivities to more complex spectral features. Here, we investigate melody recognition in a species of songbird, the European Starling (Sturnus vulgaris), using tone sequences that vary in both pitch and timbre. We find that small manipulations altering either pitch or timbre independently can drive melody recognition to chance, suggesting that both percepts are poor descriptors of the perceptual cues used by birds for this task. Instead we show that melody recognition can generalize even in the absence of pitch, as long as the spectral shapes of the constituent tones are preserved. These results challenge conventional views regarding the use of pitch cues in nonhuman auditory sequence recognition. PMID:26811447

  18. Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

    PubMed

    Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-11-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

  19. Compressed imagery detection rate through map seeking circuit, and histogram of oriented gradient pattern recognition

    NASA Astrophysics Data System (ADS)

    Newtson, Kathy A.; Creusere, Charles C.

    2017-05-01

    This research investigates the features retained after image compression for automatic pattern recognition purposes. Many raw images with vehicles in them were collected for these experiments. These raw images were significantly compressed using open-source JPEG and JPEG2000 compression algorithms. The original and compressed images are processed with a Map Seeking Circuit (MSC) pattern recognition algorithm, as well as a Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) pattern recognition program. Detection rates are given for these images that demonstrates the feature extraction capabilities as well as false alarm rates when the compression was increased. JPEG2000 compression results show preservation of the features needed for automatic pattern recognition which was better than the JPEG standard image compression results.

  20. Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

    USDA-ARS?s Scientific Manuscript database

    The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

  1. Proceedings of the NASA Symposium on Mathematical Pattern Recognition and Image Analysis

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr.

    1983-01-01

    The application of mathematical and statistical analyses techniques to imagery obtained by remote sensors is described by Principal Investigators. Scene-to-map registration, geometric rectification, and image matching are among the pattern recognition aspects discussed.

  2. The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

    NASA Astrophysics Data System (ADS)

    Marshall, J. S.; Blake, A. S. T.; Thomson, M. A.; Escudero, L.; de Vries, J.; Weston, J.; MicroBooNE collaboration

    2017-09-01

    The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. The Pandora Software Development Kit provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each address a specific task in a particular topology; a series of many tens of algorithms then carefully builds-up a picture of the event. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction.

  3. The application of pattern recognition in wood processing industry

    NASA Astrophysics Data System (ADS)

    Wang, Yeqin; Wang, Hui

    2010-08-01

    In order to improve the level of automation in wood production, the parameters of Gray level co-occurrence matrix(GLCM) and Gauss - Markov random field(GMRF) were extracted. S - NFS algorithm was applied to data fusion. And the redundancy and complementarities of two texture parameters were used to build the wood texture parameter system. An integrated measurement rule based on BP neural network classifier's overall recognition rate of samples was advanced to design its integrated classifier. Experiments show that the recognition rate of integrated neural network classifier is superior to the individual network and the nearby classifier, and the average recognition rate of 10 texture samples have reached up to 97%, which could meet the needs of industrial production. And it shows that the established parameter system for wood texture description is effective.

  4. Rotation, scale and translation invariant pattern recognition system for color images

    NASA Astrophysics Data System (ADS)

    Barajas-García, Carolina; Solorza-Calderón, Selene; Álvarez-Borrego, Josué

    2016-12-01

    This work presents a color image pattern recognition system invariant to rotation, scale and translation. The system works with three 1D signatures, one for each RGB color channel. The signatures are constructed based on Fourier transform, analytic Fourier-Mellin transform and Hilbert binary rings mask. According with the statistical theory of box-plots, the pattern recognition system has a confidence level at least of 95.4%.

  5. Binary optical filters for scale invariant pattern recognition

    NASA Technical Reports Server (NTRS)

    Reid, Max B.; Downie, John D.; Hine, Butler P.

    1992-01-01

    Binary synthetic discriminant function (BSDF) optical filters which are invariant to scale changes in the target object of more than 50 percent are demonstrated in simulation and experiment. Efficient databases of scale invariant BSDF filters can be designed which discriminate between two very similar objects at any view scaled over a factor of 2 or more. The BSDF technique has considerable advantages over other methods for achieving scale invariant object recognition, as it also allows determination of the object's scale. In addition to scale, the technique can be used to design recognition systems invariant to other geometric distortions.

  6. Control chart pattern recognition using an optimized neural network and efficient features.

    PubMed

    Ebrahimzadeh, Ata; Ranaee, Vahid

    2010-07-01

    Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. The feature extraction module uses the entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system.

  7. [Improved learning capacity and discrimination performance of neural networks in pattern recognition of biosignals].

    PubMed

    Herrmann, L; Rienäcker, U

    1992-04-01

    Pattern recognition was an important goal in the early work on artificial neural networks. Without arousing dramatic speculation, the paper describes, how a "natural" method of dealing with the configuration of the input layer can considerably improve learning behaviour and classification rate of a modified multi-layered perception with backpropagation of the error learning rule. Using this method, recognition of complex patterns in electrophysiological signals can be performed more accurately, without rules or complicated heuristic procedures. The proposed technique is demonstrated using recognition of the J-point in the ECG as an example.

  8. Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

    PubMed Central

    Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

    2015-01-01

    Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

  9. Fast traffic sign recognition with a rotation invariant binary pattern based feature.

    PubMed

    Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

    2015-01-19

    Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

  10. PTX3, a humoral pattern recognition molecule at the interface between microbe and matrix recognition.

    PubMed

    Garlanda, Cecilia; Jaillon, Sebastien; Doni, Andrea; Bottazzi, Barbara; Mantovani, Alberto

    2016-02-01

    Innate immunity consists of a cellular and a humoral arm. PTX3 is a fluid patter recognition molecule (PRM) with antibody-like properties. Gene targeted mice and genetic associations in humans suggest that PTX3 plays a non-redundant role in resistance against selected pathogens (e.g. Aspergillus fumigatus, Pseudomonas aeruginosa, uropathogenic Escherichia coli) and in the regulation of inflammation. PTX3 acts as an extrinsic oncosuppressor by taming complement elicited tumor-promoting inflammation. Recent results indicate that, by interacting with provisional matrix components, PTX3 contributes to the orchestration of tissue repair. An acidic pH sets PTX3 in a tissue repair mode, while retaining anti-microbial recognition. Based on these data and scattered information on humoral PRM and matrix components, we surmise that matrix and microbial recognition are related functions in evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    PubMed Central

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  12. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

    PubMed

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  13. All-digital ring-wedge detector applied to fingerprint recognition.

    PubMed

    Berfanger, D M; George, N

    1999-01-10

    An all-digital ring-wedge detector system is presented that simulates the analog multielement array commonly used in coherent optoelectronic processors. The system is applicable with either hard-copy or digital imagery. Using neural-network software, we demonstrate high accuracy for the recognition of fingerprints, including both orientation and wide-scale size-independent sortings by using ring-only and wedge-only input neurons, respectively. Also, the system is applied on windowed subregions of fingerprint imagery, providing a feature set that summarizes localized information about spatial-frequency content and edge-angle correlations. Examples are presented in which this localized spatial-frequency information is used to produce local ridge-orientation maps and to detect regions of poor print quality. In summary, both direct-image data and spatial-transform data are found to be important.

  14. All-Digital Ring-Wedge Detector Applied to Fingerprint Recognition

    NASA Astrophysics Data System (ADS)

    Berfanger, David M.; George, Nicholas

    1999-01-01

    An all-digital ring-wedge detector system is presented that simulates the analog multielement array commonly used in coherent optoelectronic processors. The system is applicable with either hard-copy or digital imagery. Using neural-network software, we demonstrate high accuracy for the recognition of fingerprints, including both orientation and wide-scale size-independent sortings by using ring-only and wedge-only input neurons, respectively. Also, the system is applied on windowed subregions of fingerprint imagery, providing a feature set that summarizes localized information about spatial-frequency content and edge-angle correlations. Examples are presented in which this localized spatial-frequency information is used to produce local ridge-orientation maps and to detect regions of poor print quality. In summary, both direct-image data and spatial-transform data are found to be important.

  15. Compression of digital holograms for three-dimensional object reconstruction and recognition.

    PubMed

    Naughton, Thomas J; Frauel, Yann; Javidi, Bahram; Tajahuerce, Enrique

    2002-07-10

    We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

  16. Adaptive noise cancellation based on beehive pattern evolutionary digital filter

    NASA Astrophysics Data System (ADS)

    Zhou, Xiaojun; Shao, Yimin

    2014-01-01

    Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.

  17. The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Zheng; Liu, Chen; Shen, Wensheng; Dong, Zhen; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

    2017-04-01

    A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

  18. A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses

    PubMed Central

    Powell, Michael A.; Thakor, Nitish V.

    2012-01-01

    Pattern recognition-based control of myoelectric prostheses offers amputees a natural, intuitive way of controlling the increasing functionality of modern myoelectric prostheses. While this approach to prosthesis control is certainly attractive, it is a significant departure from existing control methods. The transition from the more traditional methods of direct or proportional control to pattern recognition-based control presents a training challenge that will be unique to each amputee. In this paper we describe specific ways that a transradial amputee, prosthetist, and occupational therapist team can overcome these challenges by developing consistent and distinguishable muscle patterns. A central part of this process is the employment of a computer-based pattern recognition training system with which an amputee can learn and improve pattern recognition skills throughout the process of prosthesis fitting and testing. We describe in detail the manner in which four transradial amputees trained to improve their pattern recognition-based control of a virtual prosthesis by focusing on building consistent, distinguishable muscle patterns. We also describe a three-phase framework for instruction and training: 1) initial demonstration and conceptual instruction, 2) in-clinic testing and initial training, and 3) at-home training. PMID:23459166

  19. [A systematic review of digital radiography for the screening and recognition of pneumoconiosis].

    PubMed

    Wang, Huanqiang; Li, Tao

    2014-05-01

    To conduct a systematic review of studies reporting the comparison of digital radiography (DR) with conventional film-screen radiograph (FSR) in the screening and recognition of pneumoconiosis worldwide, to evaluate the feasibility of DR in the screening and recognition of pneumoconiosis, to analyze the similarity and difference between DR and FSR, to explore the main challenge to utilize DR in the future. The national and international databases were systematically searched for original articles on DR for screening and recognition of pneumoconiosis published from first Jan 1998 to first Nov 2013, making evaluation and selection of them, and qualitative data and quantitative data were extracted independently from the selected articles and systematically reviewed. Five hundred and twenty articles were found and evaluated and nine of them met the inclusion criteria of systematic review. The research time started from 2002 to 2011 whose objects mainly came from pneumoconiosis cases and dust-exposed workers and control population examined with DR and FSR using the high kV radiography from 120 to 130 kV. The chest radiographs were read at blind and random and standard control method. There were only two papers compare the validity of DR and FSR for recognition and classification of pneumoconiosis using gold standards. There were still some diversity of imaging processing and imaging reading without design and assessment using Standards for Reporting of Diagnostic Accuracy (STARD) in these researches. The evaluation index of the nine articles include detection rate of small opacities, crude agreement, Kappa value of Kappa Consistency Test, Area Under the Curve of ROC, etc. Seven of the nine selected articles estimated DR has generally produced superior image qualities compared to FSR. Four papers had a conclusion that DR could be equivalent to FSR in identification of shapes and profusion of small opacities and in classification of pneumoconiosis. Five papers considered

  20. Dentate gyrus supports slope recognition memory, shades of grey-context pattern separation and recognition memory, and CA3 supports pattern completion for object memory.

    PubMed

    Kesner, Raymond P; Kirk, Ryan A; Yu, Zhenghui; Polansky, Caitlin; Musso, Nick D

    2016-03-01

    In order to examine the role of the dorsal dentate gyrus (dDG) in slope (vertical space) recognition and possible pattern separation, various slope (vertical space) degrees were used in a novel exploratory paradigm to measure novelty detection for changes in slope (vertical space) recognition memory and slope memory pattern separation in Experiment 1. The results of the experiment indicate that control rats displayed a slope recognition memory function with a pattern separation process for slope memory that is dependent upon the magnitude of change in slope between study and test phases. In contrast, the dDG lesioned rats displayed an impairment in slope recognition memory, though because there was no significant interaction between the two groups and slope memory, a reliable pattern separation impairment for slope could not be firmly established in the DG lesioned rats. In Experiment 2, in order to determine whether, the dDG plays a role in shades of grey spatial context recognition and possible pattern separation, shades of grey were used in a novel exploratory paradigm to measure novelty detection for changes in the shades of grey context environment. The results of the experiment indicate that control rats displayed a shades of grey-context pattern separation effect across levels of separation of context (shades of grey). In contrast, the DG lesioned rats displayed a significant interaction between the two groups and levels of shades of grey suggesting impairment in a pattern separation function for levels of shades of grey. In Experiment 3 in order to determine whether the dorsal CA3 (dCA3) plays a role in object pattern completion, a new task requiring less training and using a choice that was based on choosing the correct set of objects on a two-choice discrimination task was used. The results indicated that control rats displayed a pattern completion function based on the availability of one, two, three or four cues. In contrast, the dCA3 lesioned rats

  1. Spatio-Temporal Pattern Recognition Using Hidden Markov Models

    DTIC Science & Technology

    1994-06-01

    motion. Bulpitt and Allinson have a method that uses a neural network to interpret the motion in MLDs (12). A measure of the relative position of each...Report RC-4788, IBM Thomas J. Watson Research Center, April 1974. 4. Dana H. Ballard and Christopher M. Brown. Computer Vision. Prentice-Hall, New...1987. 12. A. J. Bulpitt and N. M. Allinson . Motion perception and recognition using moving light displays. In Second International Conference on

  2. Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

    PubMed

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

    With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

  3. Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

    PubMed Central

    Swartz, R. Andrew

    2013-01-01

    This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

  4. Basic research planning in mathematical pattern recognition and image analysis

    NASA Technical Reports Server (NTRS)

    Bryant, J.; Guseman, L. F., Jr.

    1981-01-01

    Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.

  5. Position Saliency in Children's Short Term Recognition Memory for Graphic Patterns.

    ERIC Educational Resources Information Center

    Leslie, Ronald Carl

    This study presents an account of position saliency in terms of children's ability to utilize graphic information, and in particular the serial encoding of information from letters in a graphic pattern. By varying the number and position of the letters distinguishing graphic patterns (positive condition) in a short-term recognition memory (STRM)…

  6. Pattern Recognition in Neural Networks with Competing Dynamics: Coexistence of Fixed-Point and Cyclic Attractors

    PubMed Central

    Herrera-Aguilar, José L.; Larralde, Hernán; Aldana, Maximino

    2012-01-01

    We study the properties of the dynamical phase transition occurring in neural network models in which a competition between associative memory and sequential pattern recognition exists. This competition occurs through a weighted mixture of the symmetric and asymmetric parts of the synaptic matrix. Through a generating functional formalism, we determine the structure of the parameter space at non-zero temperature and near saturation (i.e., when the number of stored patterns scales with the size of the network), identifying the regions of high and weak pattern correlations, the spin-glass solutions, and the order-disorder transition between these regions. This analysis reveals that, when associative memory is dominant, smooth transitions appear between high correlated regions and spurious states. In contrast when sequential pattern recognition is stronger than associative memory, the transitions are always discontinuous. Additionally, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of the same set of patterns, there is a discontinuous transition between associative memory and sequential pattern recognition. In contrast, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of independent sets of patterns, the network is able to perform both associative memory and sequential pattern recognition for a wide range of parameter values. PMID:22900014

  7. Pattern recognition in neural networks with competing dynamics: coexistence of fixed-point and cyclic attractors.

    PubMed

    Herrera-Aguilar, José L; Larralde, Hernán; Aldana, Maximino

    2012-01-01

    We study the properties of the dynamical phase transition occurring in neural network models in which a competition between associative memory and sequential pattern recognition exists. This competition occurs through a weighted mixture of the symmetric and asymmetric parts of the synaptic matrix. Through a generating functional formalism, we determine the structure of the parameter space at non-zero temperature and near saturation (i.e., when the number of stored patterns scales with the size of the network), identifying the regions of high and weak pattern correlations, the spin-glass solutions, and the order-disorder transition between these regions. This analysis reveals that, when associative memory is dominant, smooth transitions appear between high correlated regions and spurious states. In contrast when sequential pattern recognition is stronger than associative memory, the transitions are always discontinuous. Additionally, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of the same set of patterns, there is a discontinuous transition between associative memory and sequential pattern recognition. In contrast, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of independent sets of patterns, the network is able to perform both associative memory and sequential pattern recognition for a wide range of parameter values.

  8. Application of pattern recognition techniques to the identification of aerospace acoustic sources

    NASA Technical Reports Server (NTRS)

    Fuller, Chris R.; Obrien, Walter F.; Cabell, Randolph H.

    1988-01-01

    A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation.

  9. Inhibition of pattern recognition receptor-mediated inflammation by bioactive phytochemicals

    USDA-ARS?s Scientific Manuscript database

    Emerging evidence reveals that pattern-recognition receptors (PRRs), Toll-like receptors (TLRs) and Nucleotide-binding oligomerization domain proteins (NODs) mediate both infection-induced and sterile inflammation by recognizing pathogen-associated molecular patterns (PAMPs) and endogenous molecules...

  10. Role of Delay of Feedback on Subsequent Pattern Recognition Transfer Tasks.

    ERIC Educational Resources Information Center

    Schroth, Marvin L.; Lund, Elissa

    1993-01-01

    Two experiments with 100 undergraduates investigated effects of delay of feedback on immediate and delayed transfer tasks involving different pattern recognition strategies. Delay of feedback resulted in greater retention of the concepts underlying construction of the different patterns in all transfer tasks. Results support the Kulhavy-Anderson…

  11. Research on the pattern evaluation in the digital speckle pattern interferometry

    NASA Astrophysics Data System (ADS)

    Wang, Yonghong; Li, Nan; Zhou, Hualong; Sun, Jianfei; Yang, Lianxiang

    2011-12-01

    Digital Speckle Pattern Interferometry (DSPI) is one of modern optical measurement techniques. It has the advantage of non-contacting, whole field, high measurement accuracy and without special process of the surface to be measured. With the development of computer science and electronics, DSPI is used more and more widely. This paper systematically presents the principle of speckles and measurements by DSPI. Optical measurement system based on DSPI was built for demonstrate. In order to quantitative determination of the distribution of phase change, several imaging process and pattern evaluation methods are discussed and compared. Phase shifting techniques and phase pattern filter methods also were described in detail. In this paper, some experiments results were given, which clearly demonstrate that that the methods are effective for processing pattern.

  12. A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern Search Models.

    PubMed

    Misra, Dharitri; Chen, Siyuan; Thoma, George R

    2009-01-01

    One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques.At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts.In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system.

  13. A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern Search Models

    PubMed Central

    Misra, Dharitri; Chen, Siyuan; Thoma, George R.

    2010-01-01

    One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques. At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts. In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system. PMID:21179386

  14. Pattern recognition of native plant communities: Manitou Colorado test site

    NASA Technical Reports Server (NTRS)

    Driscoll, R. S.

    1972-01-01

    Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.

  15. A robust HOG-based descriptor for pattern recognition

    NASA Astrophysics Data System (ADS)

    Diaz-Escobar, Julia; Kober, Vitaly

    2016-09-01

    The Histogram of Oriented Gradients (HOG) is a popular feature descriptor used in computer vision and image processing. The technique counts occurrences of gradient orientation in localized portions of an image. The descriptor is sensible to the presence in images of noise, nonuniform illumination, and low contrast. In this work, we propose a robust HOG-based descriptor using the local energy model and phase congruency approach. Computer simulation results are presented for recognition of objects in images affected by additive noise, nonuniform illumination, and geometric distortions using the proposed and conventional HOG descriptors.

  16. Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy.

    PubMed

    He, Xuefei; Nguyen, Chuong Vinh; Pratap, Mrinalini; Zheng, Yujie; Wang, Yi; Nisbet, David R; Williams, Richard J; Rug, Melanie; Maier, Alexander G; Lee, Woei Ming

    2016-08-01

    Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding method to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here paves way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.

  17. Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

    PubMed Central

    He, Xuefei; Nguyen, Chuong Vinh; Pratap, Mrinalini; Zheng, Yujie; Wang, Yi; Nisbet, David R.; Williams, Richard J.; Rug, Melanie; Maier, Alexander G.; Lee, Woei Ming

    2016-01-01

    Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding method to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here paves way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures. PMID:27570702

  18. Artificial neural network for bubbles pattern recognition on the images

    NASA Astrophysics Data System (ADS)

    Poletaev, I. E.; Pervunin, K. S.; Tokarev, M. P.

    2016-10-01

    Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques.

  19. Pattern recognition of abnormal left ventricle wall motion in cardiac MR.

    PubMed

    Lu, Yingli; Radau, Perry; Connelly, Kim; Dick, Alexander; Wright, Graham

    2009-01-01

    There are four main problems that limit application of pattern recognition techniques for recognition of abnormal cardiac left ventricle (LV) wall motion: (1) Normalization of the LV's size, shape, intensity level and position; (2) defining a spatial correspondence between phases and subjects; (3) extracting features; (4) and discriminating abnormal from normal wall motion. Solving these four problems is required for application of pattern recognition techniques to classify the normal and abnormal LV wall motion. In this work, we introduce a normalization scheme to solve the first and second problems. With this scheme, LVs are normalized to the same position, size, and intensity level. Using the normalized images, we proposed an intra-segment classification criterion based on a correlation measure to solve the third and fourth problems. Application of the method to recognition of abnormal cardiac MR LV wall motion showed promising results.

  20. Finger Vein Recognition Using Local Line Binary Pattern

    PubMed Central

    Rosdi, Bakhtiar Affendi; Shing, Chai Wuh; Suandi, Shahrel Azmin

    2011-01-01

    In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP). PMID:22247670

  1. Finger vein recognition using local line binary pattern.

    PubMed

    Rosdi, Bakhtiar Affendi; Shing, Chai Wuh; Suandi, Shahrel Azmin

    2011-01-01

    In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).

  2. Contribution of Flagellin Pattern Recognition to Intestinal Inflammation during Salmonella enterica Serotype Typhimurium Infection▿

    PubMed Central

    Winter, Sebastian E.; Thiennimitr, Parameth; Nuccio, Sean-Paul; Haneda, Takeshi; Winter, Maria G.; Wilson, R. Paul; Russell, Joseph M.; Henry, Thomas; Tran, Quynh T.; Lawhon, Sara D.; Gomez, Gabriel; Bevins, Charles L.; Rüssmann, Holger; Monack, Denise M.; Adams, L. Garry; Bäumler, Andreas J.

    2009-01-01

    Salmonella enterica serotype Typhimurium causes acute inflammatory diarrhea in humans. Flagella contribute to intestinal inflammation, but the mechanism remains unclear since most mutations abrogating pattern recognition of flagellin also prevent motility and reduce bacterial invasion. To determine the contribution of flagellin pattern recognition to the generation of innate immune responses, we compared in two animal models a nonmotile, but flagellin-expressing and -secreting serotype Typhimurium strain (flgK mutant) to a nonmotile, non-flagellin-expressing strain (flgK fliC fljB mutant). In vitro, caspase-1 can be activated by cytosolic delivery of flagellin, resulting in release of the interferon gamma inducing factor interleukin-18 (IL-18). Experiments with streptomycin-pretreated caspase-1-deficient mice suggested that induction of gamma interferon expression in the murine cecum early (12 h) after serotype Typhimurium infection was caspase-1 dependent but independent of flagellin pattern recognition. In addition, mRNA levels of the CXC chemokines macrophage inflammatory protein 2 and keratinocyte-derived chemokine were markedly increased early after serotype Typhimurium infection of streptomycin-pretreated wild-type mice regardless of flagellin expression. In contrast, in bovine ligated ileal loops, flagellin pattern recognition contributed to increased mRNA levels of macrophage inflammatory protein 3α and more fluid accumulation at 2 h after infection. Collectively, our data suggest that pattern recognition of flagellin contributes to early innate host responses in the bovine ileal mucosa but not in the murine cecal mucosa. PMID:19237529

  3. Combining speech recognition software with Digital Imaging and Communications in Medicine (DICOM) workstation software on a Microsoft Windows platform.

    PubMed

    Ernst, R; Carpenter, W; Torres, W; Wheeler, S

    2001-06-01

    This presentation describes our experience in combining speech recognition software, clinical review software, and other software products on a single computer. Different processor speeds, random access memory (RAM), and computer costs were evaluated. We found that combining continuous speech recognition software with Digital Imaging and Communications in Medicine (DICOM) workstation software on the same platform is feasible and can lead to substantial savings of hardware cost. This combination optimizes use of limited workspace and can improve radiology workflow.

  4. Clarifying the role of pattern separation in schizophrenia: the role of recognition and visual discrimination deficits.

    PubMed

    Martinelli, Cristina; Shergill, Sukhwinder S

    2015-08-01

    Patients with schizophrenia show marked memory deficits which have a negative impact on their functioning and life quality. Recent models suggest that such deficits might be attributable to defective pattern separation (PS), a hippocampal-based computation involved in the differentiation of overlapping stimuli and their mnemonic representations. One previous study on the topic concluded in favour of pattern separation impairments in the illness. However, this study did not clarify whether more elementary recognition and/or visual discrimination deficits could explain observed group differences. To address this limitation we investigated pattern separation in 22 schizophrenic patients and 24 healthy controls with the use of a task requiring individuals to classify stimuli as repetitions, novel or similar compared to a previous familiarisation phase. In addition, we employed a visual discrimination task involving perceptual similarity judgments on the same images. Results revealed impaired performance in the patient group; both on baseline measure of pattern separation as well as an index of pattern separation rigidity. However, further analyses demonstrated that such differences could be fully explained by recognition and visual discrimination deficits. Our findings suggest that pattern separation in schizophrenia is predicated on earlier recognition and visual discrimination problems. Furthermore, we demonstrate that future studies on pattern separation should include appropriate measures of recognition and visual discrimination performance for the correct interpretation of their findings.

  5. Forecasting of hourly load by pattern recognition in a small area power system

    SciTech Connect

    Dehdashti-Shahrokh, A.

    1982-01-01

    An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linear deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.

  6. Recognition of distinctive patterns of gallium-67 distribution in sarcoidosis

    SciTech Connect

    Sulavik, S.B.; Spencer, R.P.; Weed, D.A.; Shapiro, H.R.; Shiue, S.T.; Castriotta, R.J. )

    1990-12-01

    Assessment of gallium-67 ({sup 67}Ga) uptake in the salivary and lacrimal glands and intrathoracic lymph nodes was made in 605 consecutive patients including 65 with sarcoidosis. A distinctive intrathoracic lymph node {sup 67}Ga uptake pattern, resembling the Greek letter lambda, was observed only in sarcoidosis (72%). Symmetrical lacrimal gland and parotid gland {sup 67}Ga uptake (panda appearance) was noted in 79% of sarcoidosis patients. A simultaneous lambda and panda pattern (62%) or a panda appearance with radiographic bilateral, symmetrical, hilar lymphadenopathy (6%) was present only in sarcoidosis patients. The presence of either of these patterns was particularly prevalent in roentgen Stages I (80%) or II (74%). We conclude that simultaneous (a) lambda and panda images, or (b) a panda image with bilateral symmetrical hilar lymphadenopathy on chest X-ray represent distinctive patterns which are highly specific for sarcoidosis, and may obviate the need for invasive diagnostic procedures.

  7. Hybrid Approaches and Industrial Applications of Pattern Recognition,

    DTIC Science & Technology

    1980-10-01

    generally carried out by human operators with the aid of high power and low power microscopes ; it includes essentially the Leads, die and wire bonding, and...view of a muLti-lens microscope . The subcell images are also assumed to be thresholded and digitized on a few gray levels. Ll 3.4 Algorithm #1: Matching...reLiabiLity physics symposium. 3. T. I. Bajenesco, Initiation a La fiabilite en electronique moderne, Masson, Paris, 1978. 4. L. F. Pau, Failure Diagnosis and

  8. Principal patterns of fractional-order differential gradients for face recognition

    NASA Astrophysics Data System (ADS)

    Yu, Lei; Cao, Qi; Zhao, Anping

    2015-01-01

    We investigate the ability of fractional-order differentiation (FD) for facial texture representation and present a local descriptor, called the principal patterns of fractional-order differential gradients (PPFDGs), for face recognition. In PPFDG, multiple FD gradient patterns of a face image are obtained utilizing multiorientation FD masks. As a result, each pixel of the face image can be represented as a high-dimensional gradient vector. Then, by employing principal component analysis to the gradient vectors over the centered neighborhood of each pixel, we capture the principal gradient patterns and meanwhile compute the corresponding orientation patterns from which oriented gradient magnitudes are computed. Histogram features are finally extracted from these oriented gradient magnitude patterns as the face representation using local binary patterns. Experimental results on face recognition technology, A.M. Martinez and R. Benavente, Extended Yale B, and labeled faces in the wild face datasets validate the effectiveness of the proposed method.

  9. From Chaos to Harmony: Responses and Signaling upon Microbial Pattern Recognition.

    PubMed

    Yu, Xiao; Feng, Baomin; He, Ping; Shan, Libo

    2017-08-04

    Pathogen- or microbe-associated molecular patterns (PAMPs/MAMPs) are detected as nonself by host pattern recognition receptors (PRRs) and activate pattern-triggered immunity (PTI). Microbial invasions often trigger the production of host-derived endogenous signals referred to as danger- or damage-associated molecular patterns (DAMPs), which are also perceived by PRRs to modulate PTI responses. Collectively, PTI contributes to host defense against infections by a broad range of pathogens. Remarkable progress has been made toward demonstrating the cellular and physiological responses upon pattern recognition, elucidating the molecular, biochemical, and genetic mechanisms of PRR activation, and dissecting the complex signaling networks that orchestrate PTI responses. In this review, we present an update on the current understanding of how plants recognize and respond to nonself patterns, a process from which the seemingly chaotic responses form into a harmonic defense.

  10. Applying local Gabor ternary pattern for video-based illumination variable face recognition

    NASA Astrophysics Data System (ADS)

    Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

    2011-12-01

    The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Due to that both Gabor feature and LTP(local ternary pattern) are testified to be robust to illumination variations, we proposed a new approach which achieved illumination variable face recognition by combining Gabor filters with LTP operator. The experimental results compared with the published results on Yale-B and CMU PIE face database of changing illumination verify the validity of the proposed method.

  11. Applying local Gabor ternary pattern for video-based illumination variable face recognition

    NASA Astrophysics Data System (ADS)

    Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

    2012-01-01

    The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Due to that both Gabor feature and LTP(local ternary pattern) are testified to be robust to illumination variations, we proposed a new approach which achieved illumination variable face recognition by combining Gabor filters with LTP operator. The experimental results compared with the published results on Yale-B and CMU PIE face database of changing illumination verify the validity of the proposed method.

  12. Movement pattern recognition in basketball free-throw shooting.

    PubMed

    Schmidt, Andrea

    2012-04-01

    The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented.

  13. Distinct patterns of viewpoint-dependent BOLD activity during common-object recognition and mental rotation.

    PubMed

    Wilson, Kevin D; Farah, Martha J

    2006-01-01

    A fundamental but unanswered question about the human visual system concerns the way in which misoriented objects are recognized. One hypothesis maintains that representations of incoming stimuli are transformed via parietally based spatial normalization mechanisms (eg mental rotation) to match view-specific representations in long-term memory. Using fMRI, we tested this hypothesis by directly comparing patterns of brain activity evoked during classic mental rotation and misoriented object recognition involving everyday objects. BOLD activity increased systematically with stimulus rotation within the ventral visual stream during object recognition and within the dorsal visual stream during mental rotation. More specifically, viewpoint-dependent activity was significantly greater in the right superior parietal lobule during mental rotation than during object recognition. In contrast, viewpoint-dependent activity was significantly greater in the right fusiform gyrus during object recognition than during mental rotation. In addition to these differences in viewpoint-dependent activity, object recognition and mental rotation produced distinct patterns of brain activity, independent of stimulus rotation: object recognition resulted in greater overall activity within ventral stream visual areas and mental rotation resulted in greater overall activity within dorsal stream visual areas. The present results are inconsistent with the hypothesis that misoriented object recognition is mediated by structures within the parietal lobe that are known to be involved in mental rotation.

  14. Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Pchelintseva, Svetlana V.; Runnova, Anastasia E.; Musatov, Vyacheslav Yu.; Hramov, Alexander E.

    2017-03-01

    In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

  15. Digital field mapping for stimulating Secondary School students in the recognition of geological features and landforms

    NASA Astrophysics Data System (ADS)

    Giardino, Marco; Magagna, Alessandra; Ferrero, Elena; Perrone, Gianluigi

    2015-04-01

    Digital field mapping has certainly provided geoscientists with the opportunity to map and gather data in the field directly using digital tools and software rather than using paper maps, notebooks and analogue devices and then subsequently transferring the data to a digital format for subsequent analysis. But, the same opportunity has to be recognized for Geoscience education, as well as for stimulating and helping students in the recognition of landforms and interpretation of the geological and geomorphological components of a landscape. More, an early exposure to mapping during school and prior to university can optimise the ability to "read" and identify uncertainty in 3d models. During 2014, about 200 Secondary School students (aged 12-15) of the Piedmont region (NW Italy) participated in a research program involving the use of mobile devices (smartphone and tablet) in the field. Students, divided in groups, used the application Trimble Outdoors Navigators for tracking a geological trail in the Sangone Valley and for taking georeferenced pictures and notes. Back to school, students downloaded the digital data in a .kml file for the visualization on Google Earth. This allowed them: to compare the hand tracked trail on a paper map with the digital trail, and to discuss about the functioning and the precision of the tools; to overlap a digital/semitransparent version of the 2D paper map (a Regional Technical Map) used during the field trip on the 2.5D landscape of Google Earth, as to help them in the interpretation of conventional symbols such as contour lines; to perceive the landforms seen during the field trip as a part of a more complex Pleistocene glacial landscape; to understand the classical and innovative contributions from different geoscientific disciplines to the generation of a 3D structural geological model of the Rivoli-Avigliana Morainic Amphitheatre. In 2013 and 2014, some other pilot projects have been carried out in different areas of the

  16. Clustering-based pattern recognition applied to chemical recognition using SAW array signals

    SciTech Connect

    Osbourn, G.C.; Bartholomew, J.W.; Frye, G.C.; Ricco, A.J.

    1994-05-01

    We present a new patter recognition (PR) technique for chemical identification using arrays of microsensors. The technique relies on a new empirical approach to k-dimensional cluster analysis which incorporates measured human visual perceptions of difficult 2- dimensional clusters. The method can handle nonlinear SAW array data, detects both unexpected (outlier) and unreliable array responses, and has no user-adjustable parameters. We use this technique to guide the development of arrays of thin-film-coated SAW (Surface Acoustic Wave) devices that produce optimal PR performance for distinguishing a variety of volatile organic compounds, organophosphonates and water.

  17. Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

    NASA Astrophysics Data System (ADS)

    Radic, V.; Unglert, K.; Jellinek, M.

    2016-12-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

  18. High-voltage cable insulation online monitoring in coal mine based on pattern recognition

    NASA Astrophysics Data System (ADS)

    Zhao, Yongmei; Li, Junfeng; Wu, Lingjie; Wang, Yanwen

    2017-03-01

    The single-phase grounding fault is the main electrical fault types of the mine power grid. A new cable insulation online monitoring based on pattern recognition is proposed, in case single-phase grounding fault in coal mine. Firstly, using the pattern recognition method, the insulation state of the cable is divided into three types: "good insulation" and "insulation decline symmetrically" and "insulation decline asymmetrically". Then the cables with "insulation decline asymmetrically" can be further analysed and calculated and its insulation parameter value can be determined. The algorithm is simulated and verified. Simulation result shows that: The zero-sequence voltage and each phase voltage and the zero-sequence current of each cable are taken in the coal mine high-voltage system, and the insulation parameter value of each cable can be calculated accurately by using the pattern recognition method.

  19. Possible use of pattern recognition for the analysis of Mars rover X-ray fluorescence spectra

    NASA Technical Reports Server (NTRS)

    Yin, Lo I; Trombka, Jacob I.; Seltzer, Stephen M.; Johnson, Robert G.; Philpotts, John A.

    1989-01-01

    On the Mars rover sample-return mission, the rover vehicle will collect and select samples from different locations on the Martian surface to be brought back to earth for laboratory studies. It is anticipated that an in situ energy-dispersive X-ray fluorescence (XRF) spectrometer will be on board the rover. On such a mission, sample selection is of higher priority than in situ quantitative chemical anlaysis. With this in mind, a pattern recognition technique is proposed as a simple, direct, and speedy alternative to detailed chemical analysis of the XRF spectra. The validity and efficacy of the pattern recognition technique are demonstrated by the analyses of laboratory XRF spectra obtained from a series of geological samples, in the form both of standardized pressed pellets and as unprepared rocks. It is found that pattern recognition techniques applied to the raw XRF spectra can provide for the same discrimination among samples as a knowledge of their actual chemical composition.

  20. Pattern recognition based on the correlated intensity fluctuations of thermal light.

    PubMed

    Liu, Yi-Kuo; Wang, Ying; Cao, De-Zhong; Zhang, Su-Heng

    2014-07-01

    Here we present a pattern recognition scheme based on the intensity correlation of thermal light. We prove theoretically that under spatially incoherent illumination the matched filtering technique can be realized in the ghost imaging field. Using the matched filtering technique, it is possible to distinguish an object from a preestablished set of objects through their ghost images, which are extracted by means of intensity correlation measurement. According to the pattern recognition scheme, we present a numerical simulation in which we can easily identify the character inserted into the object arm from a set of two characters through the position of the autocorrelation peak. This pattern recognition scheme opens up the possibility of performing coherent optical processing under spatially incoherent illumination.

  1. Pattern recognition of HER-1 in biological fluids using stochastic sensing.

    PubMed

    Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; Gavan, Camelia Stanciu

    2015-04-01

    Stochastic sensing was employed for pattern recognition of HER-1 in biological fluids. Nanostructured materials such as 5,10,15,20-tetraphenyl-21H,23H-porphyrin, maltodextrin and α-cyclodextrin were used to modify diamond paste for stochastic sensing of HER-1. Pattern recognition of HER-1 in biological fluids was performed in a linear concentration range between 5.60 × 10(-11) and 9.72 × 10(-7 )mg ml(-1). The lower limits of determination (10(-12 )mg ml(-1) magnitude order) were recorded when maltodextrin and α-cyclodextrin were used for stochastic sensing. The pattern recognition test of HER-1 in biological fluids samples shows high reliability for both qualitative and quantitative assay.

  2. Possible use of pattern recognition for the analysis of Mars rover X ray fluorescence spectra

    NASA Astrophysics Data System (ADS)

    Trombka, Jacob I.; Seltzer, Stephen M.; Johnson, Robert G.; Philpotts, John A.

    1989-10-01

    On the Mars rover sample return mission the rover vehicle will collect and select samples from different locations on the Martial surface to be brought back to Earth for laboratory studies. It is anticipated that an in situ energy-dispersive X ray fluorescence (XRF) spectrometer will be on board the rover. On such a mission, sample selection is of higher priority than in situ quantitative chemical analysis. With this in mind we propose pattern recognition as a simple, direct, and speedy alternative to detailed chemical analysis of the XRF spectra. The validity and efficacy of the pattern recognition technique are demonstrated by the analyses of laboratory XRF spectra obtained from a series of geological samples, in the form both of standardized pressed pellets and as unprepared rocks. It is found that pattern recognition techniques applied to the raw XRF spectra can provide for the same discrimination among samples as knowledge of their actual chemical composition.

  3. Comparison of passive ranging integral imaging and active imaging digital holography for three-dimensional object recognition.

    PubMed

    Frauel, Yann; Tajahuerce, Enrique; Matoba, Osamu; Castro, Albertina; Javidi, Bahram

    2004-01-10

    We present an overview of three-dimensional (3D) object recognition techniques that use active sensing by interferometric imaging (digital holography) and passive sensing by integral imaging. We describe how each technique can be used to retrieve the depth information of a 3D scene and how this information can then be used for 3D object recognition. We explore various algorithms for 3D recognition such as nonlinear correlation and target distortion tolerance. We also provide a comparison of the advantages and disadvantages of the two techniques.

  4. Spectral pattern recognition in under-sampled functions

    SciTech Connect

    Shurtz, R.F.

    1988-08-01

    Fourier optics and an optical bench model are used to construct an ensemble of candidate functions representing variational patterns in an undersampled two dimensional function g(x,y). The known sample function s(x,y) is the product of g(x,y) and a set of unit impulses on the sample point pattern p(x,y) which, from the optical point of view, is an aperture imposing strict mathematical limits on what the sample can tell g(x,y). The laws of optics enforce much needed - and often lacking - conceptual discipline in reconstructing candidate variational patterns in g(x,y). The Fourier transform (FT) of s(x,y) is the convolution of the FT's of g(x,y) and p(x,y). If the convolution shows aliasing or confounding of frequencies undersampling is surely present and all reconstructions are indeterminate. Then information from outside s(x,y) is required and it is easily expressed in frequency terms so that the principles of optical filtering and image reconstruction can be applied. In the application described and pictured the FT of s(x,y) was filtered to eliminate unlikely or uninteresting high frequency amplitude maxima. A menu of the 100 strongest remaining terms was taken as indicating the principle variations patterns in g(x,y). Subsets of 10 terms from the menu were chosen using stepwise regression. By so restricting the subset size both the variance and the span of their inverse transforms were made consistent with those of the data. The amplitudes of the patterns being overdetermined, it was possible to estimate the phases also. The inverse transforms of 9 patterns so selected are regarded as ensembles of reconstructions, that is as stochastic process models, from which estimates of the mean and other moments can be calculated.

  5. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    PubMed Central

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-01-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. PMID:27874024

  6. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  7. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

    PubMed

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-22

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  8. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition.

    PubMed

    Zhang, Baochang; Shan, Shiguang; Chen, Xilin; Gao, Wen

    2007-01-01

    A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate.

  9. Micro-pattern gas detectors for digital radiography

    NASA Astrophysics Data System (ADS)

    Altunbas, Mustafa Cem

    The gas proportional counter technology has seen vast changes in the last decade and has lead to the introduction of micro-pattern gas detectors that offer much more improved x-ray counting rate capability and better position resolution than traditional wire chambers. In this study, we investigated the Gas Electron Multiplier, a member of micro-pattern gas detector generation, as an energy sensitive, single photon-counting x-ray detector for digital radiography. We built a high pressure detector with two cascaded Gas Electron Multipliers to evaluate its performance characteristics in Neon, Argon, Krypton and Xenon mixtures. The determination of detector gain as a function of pressure and the filling gas formed the major part of this evaluation. In the second part of the study, we employed position sensitive electronics to demonstrate the imaging capability of the detector. For the first time, we acquired 2D images and examined the spatial resolution of the detector for Krypton and Xenon mixtures as a function of gas pressure using double and quadruple GEM configurations up to 7 atmospheres gas pressure. Besides the experimental studies, we constructed a theoretical model based on the Cascaded Linear Systems approach to study the imaging performance of single photon counting gas detectors. In the model, we gave the emphasis to the intrinsic properties of the gas absorber; therefore we considered the contribution of quantum efficiency, primary electron range and reabsorption of K fluorescence photons on detector MTF and DQE.

  10. STANSORT - Stanford Remote Sensing Laboratory pattern recognition and classification system

    NASA Technical Reports Server (NTRS)

    Honey, F. R.; Prelat, A.; Lyon, R. J. P.

    1974-01-01

    The principal barrier to routine use of the ERTS multispectral scanner computer compatible tapes, rather than photointerpretation examination of the images, has been the high computing costs involved due to the large quantity of information (4 Mbytes) contained in a scene. STANSORT, the interactive program package developed at Stanford Remote Sensing Laboratories alleviates this problem, providing an extremely rapid, flexible and low cost tool for data reduction, scene classification, species searches and edge detection. The primary classification procedure, utilizing a search with variable gate widths, for similarities in the normalized, digitized spectra is described along with associated procedures for data refinement and extraction of information. The more rigorous statistical classification procedures are also explained.

  11. Autonomous attitude estimation via star sensing and pattern recognition

    NASA Technical Reports Server (NTRS)

    Junkins, J. L.; Strikwerda, T. E.

    1979-01-01

    Results are reported on the development of an autonomous, onboard, near real time spacecraft attitude estimation technique. The approach uses CCD based star sensors to digitize relative star positions. Three microcomputers are envisioned, configured in parallel, to: (1) determine star image centroids and delete spurious images; (2) identify measured stars with stars in an onboard catalog and determine discrete attitude estimates; (3) integrate gyro rate measurements and determine optimal real time attitude estimates for use in the control system and for feedback to the star identification algorithm. Algorithms for the star identification are presented. The discrete attitude estimation algorithm recovers thermally varying interlock angles between two star sensors. The optimal state estimation process recovers rate gyro biases in addition to real time attitude estimates.

  12. STANSORT - Stanford Remote Sensing Laboratory pattern recognition and classification system

    NASA Technical Reports Server (NTRS)

    Honey, F. R.; Prelat, A.; Lyon, R. J. P.

    1974-01-01

    The principal barrier to routine use of the ERTS multispectral scanner computer compatible tapes, rather than photointerpretation examination of the images, has been the high computing costs involved due to the large quantity of information (4 Mbytes) contained in a scene. STANSORT, the interactive program package developed at Stanford Remote Sensing Laboratories alleviates this problem, providing an extremely rapid, flexible and low cost tool for data reduction, scene classification, species searches and edge detection. The primary classification procedure, utilizing a search with variable gate widths, for similarities in the normalized, digitized spectra is described along with associated procedures for data refinement and extraction of information. The more rigorous statistical classification procedures are also explained.

  13. Analysis of the hand vein pattern for people recognition

    NASA Astrophysics Data System (ADS)

    Castro-Ortega, R.; Toxqui-Quitl, C.; Cristóbal, G.; Marcos, J. Victor; Padilla-Vivanco, A.; Hurtado Pérez, R.

    2015-09-01

    The shape of the hand vascular pattern contains useful and unique features that can be used for identifying and authenticating people, with applications in access control, medicine and financial services. In this work, an optical system for the image acquisition of the hand vascular pattern is implemented. It consists of a CCD camera with sensitivity in the IR and a light source with emission in the 880 nm. The IR radiation interacts with the desoxyhemoglobin, hemoglobin and water present in the blood of the veins, making possible to see the vein pattern underneath skin. The segmentation of the Region Of Interest (ROI) is achieved using geometrical moments locating the centroid of an image. For enhancement of the vein pattern we use the technique of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE). In order to remove unnecessary information such as body hair and skinfolds, a low pass filter is implemented. A method based on geometric moments is used to obtain the invariant descriptors of the input images. The classification task is achieved using Artificial Neural Networks (ANN) and K-Nearest Neighbors (K-nn) algorithms. Experimental results using our database show a percentage of correct classification, higher of 86.36% with ANN for 912 images of 38 people with 12 versions each one.

  14. An Efficient and Robust Singular Value Method for Star Pattern Recognition and Attitude Determination

    NASA Technical Reports Server (NTRS)

    Juang, Jer-Nan; Kim, Hye-Young; Junkins, John L.

    2003-01-01

    A new star pattern recognition method is developed using singular value decomposition of a measured unit column vector matrix in a measurement frame and the corresponding cataloged vector matrix in a reference frame. It is shown that singular values and right singular vectors are invariant with respect to coordinate transformation and robust under uncertainty. One advantage of singular value comparison is that a pairing process for individual measured and cataloged stars is not necessary, and the attitude estimation and pattern recognition process are not separated. An associated method for mission catalog design is introduced and simulation results are presented.

  15. Compressed imagery detection rate through map seeking circuit (MSC) pattern recognition

    NASA Astrophysics Data System (ADS)

    Newtson, Kathy A.; Creusere, Charles C.

    2016-05-01

    This research investigates the features retained after image compression for automatic pattern recognition purposes. Images were significantly compressed using open-source JPEG and JPEG2000 compression algorithms. The original and compressed images were processed with a Map Seeking Circuit (MSC) pattern recognition algorithm. [1] The resulting target detection rates for the compressed images were very similar to the original images, which included compression rates ranging from 10 to 0.2. Target detection location precision and target aspect were degraded for the lowest compression rates.

  16. Oxidized LDL: Diversity, Patterns of Recognition, and Pathophysiology

    PubMed Central

    Volkov, Suncica; Subbaiah, Papasani V.

    2010-01-01

    Abstract Oxidative modification of LDL is known to elicit an array of pro-atherogenic responses, but it is generally underappreciated that oxidized LDL (OxLDL) exists in multiple forms, characterized by different degrees of oxidation and different mixtures of bioactive components. The variable effects of OxLDL reported in the literature can be attributed in large part to the heterogeneous nature of the preparations employed. In this review, we first describe the various subclasses and molecular composition of OxLDL, including the variety of minimally modified LDL preparations. We then describe multiple receptors that recognize various species of OxLDL and discuss the mechanisms responsible for the recognition by specific receptors. Furthermore, we discuss the contentious issues such as the nature of OxLDL in vivo and the physiological oxidizing agents, whether oxidation of LDL is a prerequisite for atherogenesis, whether OxLDL is the major source of lipids in foam cells, whether in some cases it actually induces cholesterol depletion, and finally the Janus-like nature of OxLDL in having both pro- and anti-inflammatory effects. Lastly, we extend our review to discuss the role of LDL oxidation in diseases other than atherosclerosis, including diabetes mellitus, and several autoimmune diseases, such as lupus erythematosus, anti-phospholipid syndrome, and rheumatoid arthritis. Antioxid. Redox Signal. 13, 39–75. PMID:19888833

  17. Kernel Wiener filter and its application to pattern recognition.

    PubMed

    Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

    2010-11-01

    The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

  18. Pattern Recognition on Read Positioning in Next Generation Sequencing

    PubMed Central

    Byeon, Boseon; Kovalchuk, Igor

    2016-01-01

    The usefulness and the utility of the next generation sequencing (NGS) technology are based on the assumption that the DNA or cDNA cleavage required to generate short sequence reads is random. Several previous reports suggest the existence of sequencing bias of NGS reads. To address this question in greater detail, we analyze NGS data from four organisms with different GC content, Plasmodium falciparum (19.39%), Arabidopsis thaliana (36.03%), Homo sapiens (40.91%) and Streptomyces coelicolor (72.00%). Using machine learning techniques, we recognize the pattern that the NGS read start is positioned in the local region where the nucleotide distribution is dissimilar from the global nucleotide distribution. We also demonstrate that the mono-nucleotide distribution underestimates sequencing bias, and the recognized pattern is explained largely by the distribution of multi-nucleotides (di-, tri-, and tetra- nucleotides) rather than mono-nucleotides. This implies that the correction of sequencing bias needs to be performed on the basis of the multi-nucleotide distribution. Providing companion software to quantify the effect of the recognized pattern on read positioning, we exemplify that the bias correction based on the mono-nucleotide distribution may not be sufficient to clean sequencing bias. PMID:27299343

  19. A practical application of computer pattern recognition research: the Abbott ADC-500 differential classifier.

    PubMed

    Green, J E

    1979-01-01

    The ADC-500 is a new blood cell differential classifier manufactured by Abbott Laboratories. It performs 500-cell leukocyte differentials on both normal and abnormal cells, evaluates red cell morphology and estimates platelet sufficiency at a rate of 40 to 50 samples per hour in stand-alone operation. The ADC-500 system consists of a spinner which prepares a uniform blood monolayer on a slide, a stainer which reproducibly stains the slide with Wright's stain, an encoder which attaches an instrument and human readable identification to the slide and an analyzer which accepts a stack of up to 50 slides, evaluates these slides and prints the results and the slide identification on report forms. The system's analysis rate, which represents a 5- to 10-fold increase over other commercially available differential counters, requires a number of specialized techniques for its realization. One key to this performance is the development of a high speed X-Y slide positioning stage which can move to a new cell and settle in 50 msec. Another is the high degree of parallelism used in the system structure and the pipelining of the data processing. A third is the development of uniform and repeatable sample preparation modules. Within the analyzer module, the autofocus, white cell acquisition and high resolution cell analysis systems are independent and operate in parallel. At the same time within the high resolution cell analysis system, one cell is acquired; the digitized image of a second processed; and a third is classified using pattern recognition techniques. All of these tasks, except focus, are under the control of a minicomputer system. Tests of the system reveal good accuracy and an improvement in precision due to the increase in the number of counted cells.

  20. A morphological descriptors-based pattern recognition system for the characterization of hip osteoarthritis severity from X-ray images

    NASA Astrophysics Data System (ADS)

    Boniatis, I.; Costaridou, L.; Cavouras, D.; Panagiotopoulos, E.; Panayiotakis, G.

    2007-10-01

    A pattern recognition system is proposed for the characterization of hip osteoarthritis (OA) severity. Sixty-four (64) hips, corresponding to 32 unilateral and bilateral OA patients were studied. Employing the Kellgren and Lawrence scale, hips were grouped into three OA severity categories: "Normal", "Mild/Moderate", and "Severe". Utilizing custom-developed software, 64 ROIs, corresponding to patients' radiographic Hip Joint Spaces (HJSs), were determined on digitized radiographs. A Probabilistic Neural Network classifier was designed employing morphological descriptors of the HJS-ROIs. The classifier discriminated successfully between (i) normal and OA hips (92.2% accuracy) and (ii) hips of "Mild/Moderate" OA and of "Severe" OA (91.3% accuracy). The proposed system could contribute in assessing hip OA severity.

  1. Effect of Digital Frequency Compression (DFC) on Speech Recognition in Candidates for Combined Electric and Acoustic Stimulation (EAS)

    ERIC Educational Resources Information Center

    Gifford, Rene H.; Dorman, Michael F.; Spahr, Anthony J.; McKarns, Sharon A.

    2007-01-01

    Purpose: To compare the effects of conventional amplification (CA) and digital frequency compression (DFC) amplification on the speech recognition abilities of candidates for a partial-insertion cochlear implant, that is, candidates for combined electric and acoustic stimulation (EAS). Method: The participants were 6 patients whose audiometric…

  2. Effect of Digital Frequency Compression (DFC) on Speech Recognition in Candidates for Combined Electric and Acoustic Stimulation (EAS)

    ERIC Educational Resources Information Center

    Gifford, Rene H.; Dorman, Michael F.; Spahr, Anthony J.; McKarns, Sharon A.

    2007-01-01

    Purpose: To compare the effects of conventional amplification (CA) and digital frequency compression (DFC) amplification on the speech recognition abilities of candidates for a partial-insertion cochlear implant, that is, candidates for combined electric and acoustic stimulation (EAS). Method: The participants were 6 patients whose audiometric…

  3. Assessing speech recognition abilities with digits in noise in cochlear implant and hearing aid users.

    PubMed

    Kaandorp, Marre W; Smits, Cas; Merkus, Paul; Goverts, S Theo; Festen, Joost M

    2015-01-01

    The primary objective of the study was to investigate the feasibility, reliability, and validity of the Dutch digits in noise (DIN) test for measuring speech recognition in hearing aid and cochlear implant users and compare results to the standard sentences-in-noise (SIN) test. The relation between speech reception thresholds for DIN test and SIN test was analysed to determine the validity of the DIN test. As linguistic skills were expected to make different contributions in these tests, their influence was analysed. Participants were 12 normal-hearing listeners, 24 hearing aid users, and 24 cochlear implant users. The DIN test was feasible for more participants than the SIN test. Intraclass correlation coefficients showed high reliability. The standard error of measurement was smaller for the DIN test than for the SIN test. DIN test and SIN test were highly correlated (r = 0.95 and r = 0.56 for NH+ HA and CI users respectively). In the regression analysis no significant contribution of basic linguistic skills or personal factors was found. In the assessment of speech recognition in noise of aided hearing-impaired listeners with hearing aids or cochlear implants, the DIN test is a feasible, reliable and valid test.

  4. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    NASA Astrophysics Data System (ADS)

    Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  5. Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol.

    PubMed

    Kim, Sungho; Choi, Bongsik; Lim, Meehyun; Yoon, Jinsu; Lee, Juhee; Kim, Hee-Dong; Choi, Sung-Jin

    2017-03-28

    Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.

  6. Thrombelastographic pattern recognition in renal disease and trauma.

    PubMed

    Chapman, Michael P; Moore, Ernest E; Burneikis, Dominykas; Moore, Hunter B; Gonzalez, Eduardo; Anderson, Kelsey C; Ramos, Christopher R; Banerjee, Anirban

    2015-03-01

    Thrombelastography (TEG) is a viscoelastic hemostatic assay. We have observed that end-stage renal disease (ESRD) and trauma-induced coagulopathy (TIC) produce distinctive TEG tracings. We hypothesized that rigorously definable TEG patterns could discriminate between healthy controls and patients with ESRD and TIC. TEG was performed on blood from ESRD patients (n = 54) and blood from trauma patients requiring a massive blood transfusion (n = 16). Plots of independent TEG parameters were analyzed for patterns coupled to disease state, compared with controls. Decision trees for taxonomic classification were then built using the "R-Project" statistical software. Minimally overlapping clusters of TEG results were observed for the three patient groups when coordinate pairs of maximum amplitude (MA) and TEG-activated clotting time (ACT) were plotted on orthogonal axes. Based on these groupings, a taxonomical classification tree was constructed using MA and TEG ACT. Branch points were set at an ACT of 103 s, and these branches subdivided for MA at 60.8 mm for the high ACT branch and 72.6 mm for the low ACT branch, providing a correct classification rate of 93.4%. ESRD and TIC demonstrate distinct TEG patterns. The coagulopathy of ESRD is typified by a prolonged enzymatic phase of clot formation, with normal-to-elevated final clot strength. Conversely, TIC is typified by prolonged clot formation and weakened clot strength. Our taxonomic categorization constitutes a rigorous system for the algorithmic interpretation of TEG based on cluster analysis. This will form the basis for clinical decision support software for viscoelastic hemostatic assays. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. Statistical Pattern Recognition Techniques as Applied to Radar Returns.

    DTIC Science & Technology

    1981-12-01

    interpattern distance . To be specific, the Mahalanobis distance similarity function was used. This function normalizes distance in order to make the analysis...specifies a test strategy in terms of CT likelihood ratio 9,(v), which is a function of data v; and threshold T, which is a function of error cost...is found that is sufficiently removed from the existing structure. Let d k(i) be the distance between pattern i and cluster center k. Let d(i) be the

  8. A novel local pattern descriptor--local vector pattern in high-order derivative space for face recognition.

    PubMed

    Fan, Kuo-Chin; Hung, Tsung-Yung

    2014-07-01

    In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local derivative directions from the n th-order LVP in (n-1) th-order derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

  9. Solar and space weather phenomenological forecasting using pattern recognition operators

    NASA Astrophysics Data System (ADS)

    Rosa, R.; Ramos, F.; Vijaykumar, N.; Andrade, M.; Fernandes, F.; Cecatto, J.; Sharma, A.; Sawant, H.

    Yohkoh, SOHO and HESSI satellites have shown morphological change of the coronal magnetic structures in several scales. Particularly, the soft X ray images- have revealed the existence of dynamic structures with magnetic field configuration varying from regular to complex patterns. In order to characterize the spatio- temporal evolution of such structures, a methodology is proposed in terms of matrix computational operators to quantify the amount of symmetry breaking along the gradient field evolution of the sequence of images. Characterization of symmetry breaking in the gradient field of the energy envelope has been an useful tool to understand complex plasma regimes. In this paper we introduce the application of the Gradient Pattern Analysis (GPA) technique as a new matrix computational operator for spatio-temporal plasma gradient field analysis. This operator yields a measure of the symmetry breaking and phase disorder parameters responding to the active region plasma regimes. In order to characterize the GPA performance into the context of solar physics, we apply this technique on X-ray emission measurement from solar coronal plasma observed by means of Yohkoh satellite. The preliminary results and interpretations suggest a new phenomenological approach for the spatio- temporal evolution of soft X ray active regions, mainly those whose morphology- goes from a regular to a complex magnetic configuration a companied by thec increase of the dissipated energy. We discuss the importance of this semi-empirical modelling for space weather forecasting into the context of solar-terrestrial relationship.

  10. Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

    PubMed

    Ida, Hirofumi; Fukuhara, Kazunobu; Ishii, Motonobu

    2012-01-01

    The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

  11. Recognition of Tennis Serve Performed by a Digital Player: Comparison among Polygon, Shadow, and Stick-Figure Models

    PubMed Central

    Ida, Hirofumi; Fukuhara, Kazunobu; Ishii, Motonobu

    2012-01-01

    The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion. PMID:22439009

  12. Comparison of speech recognition with adaptive digital and FM remote microphone hearing assistance technology by listeners who use hearing aids.

    PubMed

    Thibodeau, Linda

    2014-06-01

    The purpose of this study was to compare the benefits of 3 types of remote microphone hearing assistance technology (HAT), adaptive digital broadband, adaptive frequency modulation (FM), and fixed FM, through objective and subjective measures of speech recognition in clinical and real-world settings. Participants included 11 adults, ages 16 to 78 years, with primarily moderate-to-severe bilateral hearing impairment (HI), who wore binaural behind-the-ear hearing aids; and 15 adults, ages 18 to 30 years, with normal hearing. Sentence recognition in quiet and in noise and subjective ratings were obtained in 3 conditions of wireless signal processing. Performance by the listeners with HI when using the adaptive digital technology was significantly better than that obtained with the FM technology, with the greatest benefits at the highest noise levels. The majority of listeners also preferred the digital technology when listening in a real-world noisy environment. The wireless technology allowed persons with HI to surpass persons with normal hearing in speech recognition in noise, with the greatest benefit occurring with adaptive digital technology. The use of adaptive digital technology combined with speechreading cues would allow persons with HI to engage in communication in environments that would have otherwise not been possible with traditional wireless technology.

  13. Crowding by a single bar: probing pattern recognition mechanisms in the visual periphery.

    PubMed

    Põder, Endel

    2014-11-06

    Whereas visual crowding does not greatly affect the detection of the presence of simple visual features, it heavily inhibits combining them into recognizable objects. Still, crowding effects have rarely been directly related to general pattern recognition mechanisms. In this study, pattern recognition mechanisms in visual periphery were probed using a single crowding feature. Observers had to identify the orientation of a rotated T presented briefly in a peripheral location. Adjacent to the target, a single bar was presented. The bar was either horizontal or vertical and located in a random direction from the target. It appears that such a crowding bar has very strong and regular effects on the identification of the target orientation. The observer's responses are determined by approximate relative positions of basic visual features; exact image-based similarity to the target is not important. A version of the "standard model" of object recognition with second-order features explains the main regularities of the data. © 2014 ARVO.

  14. Study on local Gabor binary patterns for face representation and recognition

    NASA Astrophysics Data System (ADS)

    Ge, Wei; Han, Chunling; Quan, Wei

    2015-12-01

    More recently, Local Binary Patterns(LBP) has received much attention in face representation and recognition. The original LBP operator could describe the spatial structure information, which are the variety edge or variety angle features of local facial images essentially, they are important factors of classify different faces. But the scale and orientation of the edge features include more detail information which could be used to classify different persons efficiently, while original LBP operator could not to extract the information. In this paper, based on the introduction of original LBP-based facial representation and recognition, the histogram sequences of local Gabor binary patterns are used to representation facial image. Principal Component Analysis (PCA) method is used to classification the histogram sequences, which have been converted to vectors. Recognition experimental results show that the method we used in this paper increases nearly 6% than the classification performance of original LBP operator.

  15. Summary of the transfer of optical processing to systems: optical pattern recognition program

    NASA Astrophysics Data System (ADS)

    Lindell, Scott D.

    1995-06-01

    Martin Marietta has successfully completed a TOPS optical pattern recognition program. The program culminated in August 1994 with an automatic target recognition flight demonstration inwhich an M60A2 tank was acquired, identified, and tracked with a visible seeker from a UH-1 helicopter flying a fiber optic guided missile (FOG-M) mission profile. The flight demonstration was conducted by the US Army Missile Command (MICOM) and supported by Martin Marietta. The pattern recognition system performance for acquiring and identifying the M60A2 tank, which was positioned among an array with five other vehicle types, was 90% probability of correct identification and a 4% false identification for over 40,000 frames of imagery processed. Imagery was processed at a 15 Hz input rate with a 1 ft3, 76 W, 4 GFLOP processor performing up to 800 correlations per second.

  16. The ART of adaptive pattern recognition by a self-organizing neural network

    SciTech Connect

    Carpenter, G.A.; Grossberg, S.

    1988-03-01

    The Adaptive Resonance Theory (ART) architectures discussed here are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. This article outlines the main computational properties of these ART architectures, while comparing and contrasting these properties with those of alternative learning and recognition systems. Technical details are described in greater detail elsewhere, and several books collect articles in which the theory was developed through the analysis and prediction of interdisciplinary data about the brain and behavior.

  17. Study on the classification algorithm of degree of arteriosclerosis based on fuzzy pattern recognition

    NASA Astrophysics Data System (ADS)

    Ding, Li; Zhou, Runjing; Liu, Guiying

    2010-08-01

    Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.

  18. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

    PubMed

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

  19. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems

    PubMed Central

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What’s more, the improved algorithm can enhance the accuracy of blind recognition obviously. PMID:26154439

  20. Foundations for a syntatic pattern recognition system for genomic DNA sequences

    SciTech Connect

    Searles, D.B.

    1993-03-01

    The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

  1. Effects of Cooperative Group Work Activities on Pre-School Children's Pattern Recognition Skills

    ERIC Educational Resources Information Center

    Tarim, Kamuran

    2015-01-01

    The aim of this research is twofold; to investigate the effects of cooperative group-based work activities on children's pattern recognition skills in pre-school and to examine the teachers' opinions about the implementation process. In line with this objective, for the study, 57 children (25 girls and 32 boys) were chosen from two private schools…

  2. Emotion Recognition by Children With Down Syndrome: Investigation of Specific Impairments and Error Patterns

    ERIC Educational Resources Information Center

    Williams, Katie R.; Wishart, Jennifer G.; Pitcairn, Tom K.; Willis, Diane S.

    2005-01-01

    The ability of children with Down syndrome to recognize expressions of emotion was compared to performance in typically developing and nonspecific intellectual disability groups matched on either MA or a performance-related measure. Our goal was to (a) resolve whether specific emotions present recognition difficulties; (b) investigate patterns of…

  3. Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition

    SciTech Connect

    Not Available

    1986-01-01

    This book presents the papers given at a conference on image processing and pattern recognition. Topics considered at the conference included stereovision, vision applications, parallel processing, algorithms, artificial intelligence, structure from motion, three-dimensional shape representation, array processors, industrial vision systems, computer architecture, homogeneous multiprocessors, shape from texture, and optimal likelihood generators for edge detection under Gaussian additive noise.

  4. Proceedings of the IEEE conference on computer vision and pattern recognition

    SciTech Connect

    Not Available

    1985-01-01

    This book presents the papers given at a conference on image processing, pattern recognition, and robotic vision. Topics considered at the conference included expert systems, artificial intelligence, knowledge bases, computerized simulation, computer architecture, vision models and texture, algorithms, parallel algorithms, data processing, circuit theory, array processors, and distributed data processing, and data-flow processing.

  5. A strip chart recorder pattern recognition tool kit for Shuttle operations

    NASA Technical Reports Server (NTRS)

    Hammen, David G.; Moebes, Travis A.; Shelton, Robert O.; Savely, Robert T.

    1993-01-01

    During Space Shuttle operations, Mission Control personnel monitor numerous mission-critical systems such as electrical power; guidance, navigation, and control; and propulsion by means of paper strip chart recorders. For example, electrical power controllers monitor strip chart recorder pen traces to identify onboard electrical equipment activations and deactivations. Recent developments in pattern recognition technologies coupled with new capabilities that distribute real-time Shuttle telemetry data to engineering workstations make it possible to develop computer applications that perform some of the low-level monitoring now performed by controllers. The number of opportunities for such applications suggests a need to build a pattern recognition tool kit to reduce software development effort through software reuse. We are building pattern recognition applications while keeping such a tool kit in mind. We demonstrated the initial prototype application, which identifies electrical equipment activations, during three recent Shuttle flights. This prototype was developed to test the viability of the basic system architecture, to evaluate the performance of several pattern recognition techniques including those based on cross-correlation, neural networks, and statistical methods, to understand the interplay between an advanced automation application and human controllers to enhance utility, and to identify capabilities needed in a more general-purpose tool kit.

  6. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    ERIC Educational Resources Information Center

    Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  7. Pattern recognition using neural networks. Technical report, August 1, 1994--September 11, 1994

    SciTech Connect

    Ma, H.

    1994-12-31

    I am pleased to submit the following technical report to Oak Ridge National Laboratories as an accomplishment of the 6 (six) week appointment in the U.S. Nuclear Regulatory Commission`s Historically Black College and Universities Faculty Research Participation Program, Summer 1994 (August - September 11, 1994). In this project, an approach for pattern recognition using neural networks is proposed. Particularly, a Boltzmann machine, a Hopfield neural net model, is used in pattern recognition with desirable learning ability. The Boltzmann machine features stochastic learning, which acts as the connection dynamics for determining the weights on the connections between the neuron-like cells (processing elements) of different layers in the neural network. An algorithm for pattern recognition using Boltzmann machine is also presented, which could be coded with C programming language or others to implement the approach for efficient pattern recognition. Finally, a follow-on research work derived from this project is planned if the author could win another summer appointment in 1995 from the Science/Engineering Education Division, Oak Ridge Institute for Science and Education, Oak Ridge National Laboratories.

  8. Designing Clinical Examples To Promote Pattern Recognition: Nursing Education-Based Research and Practical Applications.

    ERIC Educational Resources Information Center

    Welk, Dorette Sugg

    2002-01-01

    Sophomore nursing students (n=162) examined scenarios depicting typical and atypical signs of heart attack. Examples were structured to include essential and nonessential symptoms, enabling pattern recognition and improved performance. The method provides a way to prepare students to anticipate and recognize life-threatening situations. (Contains…

  9. Influence of multiple dynamic factors on the performance of myoelectric pattern recognition.

    PubMed

    Khushaba, Rami N; Al-Timemy, Ali; Kodagoda, Sarath

    2015-08-01

    Hand motion classification using surface Electromyogram (EMG) signals has been widely studied for the control of powered prosthetics in laboratory conditions. However, clinical applicability has been limited, as imposed by factors like electrodes shift, variations in the contraction force levels, forearm rotation angles, change of limb position and many other factors that all affect the EMG pattern recognition performance. While the impact of several of these factors on EMG parameter estimation and pattern recognition has been considered individually in previous studies, a minimum number of experiments were reported to study the influence of multiple dynamic factors. In this paper, we investigate the combined effect of varying forearm rotation angles and contraction force levels on the robustness of EMG pattern recognition, while utilizing different time-and-frequency based feature extraction methods. The EMG pattern recognition system has been validated on a set of 11 subjects (ten intact-limbed and one bilateral transradial amputee) performing six classes of hand motions, each with three different force levels, each at three different forearm rotation angles, with six EMG electrodes plus an accelerometer on the subjects' forearm. Our results suggest that the performance of the learning algorithms can be improved with the Time-Dependent Power Spectrum Descriptors (TD-PSD) utilized in our experiments, with average classification accuracies of up to 90% across all subjects, force levels, and forearm rotation angles.

  10. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    ERIC Educational Resources Information Center

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  11. Dual-band, infrared buried mine detection using a statistical pattern recognition approach

    SciTech Connect

    Buhl, M.R.; Hernandez, J.E.; Clark, G.A.; Sengupta, S.K.

    1993-08-01

    The main objective of this work was to detect surrogate land mines, which were buried in clay and sand, using dual-band, infrared images. A statistical pattern recognition approach was used to achieve this objective. This approach is discussed and results of applying it to real images are given.

  12. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    ERIC Educational Resources Information Center

    Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  13. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    ERIC Educational Resources Information Center

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  14. Fuzzy logic analysis optimizations for pattern recognition - Implementation and experimental results

    NASA Astrophysics Data System (ADS)

    Hires, Matej; Habiballa, Hashim

    2017-07-01

    The article presents an practical results of optimization of the fuzzy logic analysis method for pattern recognition. The theoretical background of the proposed theory is shown in the former article extending the original fuzzy logic analysis method. This article shows the implementation and experimental verification of the approach.

  15. Projection-invariant pattern recognition with a phase-only logarithmic-harmonic-derived filter.

    PubMed

    Moya, A; Mendlovic, D; García, J; Ferreira, C

    1996-07-10

    A phase-only filter based on logarithmic harmonics for projection-invariant pattern recognition is presented. This logarithmic-harmonic-derived filter is directly calculated in the Fourier plane. With respect to normal logarithmic-harmonic filters it provides a smaller variation of the correlation intensity with the projection factor of the target. Computer and optical experiments are presented.

  16. A strip chart recorder pattern recognition tool kit for Shuttle operations

    NASA Technical Reports Server (NTRS)

    Hammen, David G.; Moebes, Travis A.; Shelton, Robert O.; Savely, Robert T.

    1993-01-01

    During Space Shuttle operations, Mission Control personnel monitor numerous mission-critical systems such as electrical power; guidance, navigation, and control; and propulsion by means of paper strip chart recorders. For example, electrical power controllers monitor strip chart recorder pen traces to identify onboard electrical equipment activations and deactivations. Recent developments in pattern recognition technologies coupled with new capabilities that distribute real-time Shuttle telemetry data to engineering workstations make it possible to develop computer applications that perform some of the low-level monitoring now performed by controllers. The number of opportunities for such applications suggests a need to build a pattern recognition tool kit to reduce software development effort through software reuse. We are building pattern recognition applications while keeping such a tool kit in mind. We demonstrated the initial prototype application, which identifies electrical equipment activations, during three recent Shuttle flights. This prototype was developed to test the viability of the basic system architecture, to evaluate the performance of several pattern recognition techniques including those based on cross-correlation, neural networks, and statistical methods, to understand the interplay between an advanced automation application and human controllers to enhance utility, and to identify capabilities needed in a more general-purpose tool kit.

  17. Designing Clinical Examples To Promote Pattern Recognition: Nursing Education-Based Research and Practical Applications.

    ERIC Educational Resources Information Center

    Welk, Dorette Sugg

    2002-01-01

    Sophomore nursing students (n=162) examined scenarios depicting typical and atypical signs of heart attack. Examples were structured to include essential and nonessential symptoms, enabling pattern recognition and improved performance. The method provides a way to prepare students to anticipate and recognize life-threatening situations. (Contains…

  18. An Improved Algorithm for Linear Inequalities in Pattern Recognition and Switching Theory.

    ERIC Educational Resources Information Center

    Geary, Leo C.

    This thesis presents a new iterative algorithm for solving an n by l solution vector w, if one exists, to a set of linear inequalities, A w greater than zero which arises in pattern recognition and switching theory. The algorithm is an extension of the Ho-Kashyap algorithm, utilizing the gradient descent procedure to minimize a criterion function…

  19. Three dimensional pattern recognition using feature-based indexing and rule-based search

    NASA Astrophysics Data System (ADS)

    Lee, Jae-Kyu

    In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

  20. Auditory pattern recognition and brief tone discrimination of children with reading disorders.

    PubMed

    Walker, Marianna M; Givens, Gregg D; Cranford, Jerry L; Holbert, Don; Walker, Letitia

    2006-01-01

    Auditory pattern recognition skills in children with reading disorders were investigated using perceptual tests involving discrimination of frequency and duration tonal patterns. A behavioral test battery involving recognition of the pattern of presentation of tone triads was used in which individual components differed in either frequency or duration. A test involving measurement of difference limens for long and short duration tones was also administered. In comparison to controls, children with reading disorders exhibited significantly higher error rates in discrimination of duration and frequency patterns, as well as larger brief tone frequency difference limens. These results suggest that difficulties in the recognition and processing of auditory patterns may co-occur with decoding deficits in children with reading disorders. (1) As a result of this activity, the participant will be able to identify a relationship between reading and temporal processing. (2) As a result of this activity, the reader will be able to discuss the difference between sight-word decoding and phonological decoding. (3) As a result of this activity, the reader will be able to explain a relationship between reading skills and the identification of auditory patterns.

  1. A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration after Incomplete Cervical Spinal Cord Injury

    PubMed Central

    Liu, Jie; Zhou, Ping

    2013-01-01

    This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of 9 subjects with incomplete cervical SCI while they tried to perform 6 different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>97%) were achieved in classification of 7 different classes (6 intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury. PMID:23033334

  2. Precision grasps of children and young and old adults: individual differences in digit contact strategy, purchase pattern, and digit posture.

    PubMed

    Wong, Yvonne J; Whishaw, Ian Q

    2004-09-23

    The evolutionary origins and variations of the precision grip, in which an object is held between the thumb and other digits, are poorly understood. This is surprising because the neural basis of this grasp pattern, including the motor cortex and pyramidal tract have received extensive study. Most previous work has shown that features of an object to be grasped (external factors) determine grasp patterns. The objective of the present study was to investigate individual differences (central factors) in use of the pincer and other precision grips. The grasping patterns of male and female young adults, older adults and children were examined as they reached (with both left and right hand) for five small beads (3-16 mm diameter). Frame-by-frame analysis of grasping indicated a high degree of variability in digit contact strategies, purchase patterns and digit posture both within and between subjects. (1) The contact strategies consisted of five variations, depending on whether the thumb or the index finger dragged or stabilized the bead for grasping. (2) Purchase patterns consisted of seven different types of precision grips, involving the thumb and various combinations of other digits. (3) There were four variations stemming from the posture of the non-grasping digits. Grip patterns of the left and right hands were correlated in individual subjects, as were strategies used for different bead sizes. Females displayed slightly more variability in grasp patterns than did males, and digit width (obtained from photocopies of the subjects' hands) was weakly correlated with the grasp patterns used. Although it was expected that the pincer would be used for all objects, it was preferentially used for only the smallest object except for older adults who used the pincer grasp on most objects. The variability in digit contact strategies, purchase patterns, and posture of the non-grasping digits indicates that central factors (innate or learning-induced architecture of the left

  3. Bearing Fault Diagnostics Using the Spectral Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Pennacchi, P.; Borghesani, P.; Chatterton, S.; Ricci, R.

    In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

  4. Retinotopically specific reorganization of visual cortex for tactile pattern recognition

    PubMed Central

    Cheung, Sing-Hang; Fang, Fang; He, Sheng; Legge, Gordon E.

    2009-01-01

    Although previous studies have shown that Braille reading and other tactile-discrimination tasks activate the visual cortex of blind and sighted people [1–5], it is not known whether this kind of cross-modal reorganization is influenced by retinotopic organization. We have addressed this question by studying S, a visually impaired adult with the rare ability to read print visually and Braille by touch. S had normal visual development until age six years, and thereafter severe acuity reduction due to corneal opacification, but no evidence of visual-field loss. Functional magnetic resonance imaging (fMRI) revealed that, in S’s early visual areas, tactile information processing activated what would be the foveal representation for normally-sighted individuals, and visual information processing activated what would be the peripheral representation. Control experiments showed that this activation pattern was not due to visual imagery. S’s high-level visual areas which correspond to shape- and object-selective areas in normally-sighted individuals were activated by both visual and tactile stimuli. The retinotopically specific reorganization in early visual areas suggests an efficient redistribution of neural resources in the visual cortex. PMID:19361999

  5. Pattern recognition in volcano seismology - Reducing spectral dimensionality

    NASA Astrophysics Data System (ADS)

    Unglert, K.; Radic, V.; Jellinek, M.

    2015-12-01

    Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we evaluate whether a machine learning technique called Self-Organizing Maps (SOMs) can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. This could reduce the dimensions of the spectral space typically analyzed by orders of magnitude, and enable rapid processing and visualization. Preliminary results suggest that the temporal evolution of volcano seismicity at Kilauea Volcano, Hawai`i, can be reduced to as few as 2 spectral components by using a combination of SOMs and cluster analysis. We will further refine our methodology with several datasets from Hawai`i and Alaska, among others, and compare it to other techniques.

  6. Self-esteem recognition based on gait pattern using Kinect.

    PubMed

    Sun, Bingli; Zhang, Zhan; Liu, Xingyun; Hu, Bin; Zhu, Tingshao

    2017-09-08

    Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem. 178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score. After completing the RRS, each participant walks for two minutes naturally on a rectangular red carpet, and the gait data are recorded using Kinect sensor. After data preprocessing, we extract a few behavioral features to train predicting model by machine learning. Based on these features, we build predicting models to recognize self-esteem. For self-esteem prediction, the best correlation coefficient between predicted score and self-report score is 0.45 (p<0.001). We divide the participants according to gender, and for males, the correlation coefficient is 0.43 (p<0.001), for females, it is 0.59 (p<0.001). Using gait data captured by Kinect sensor, we find that the gait pattern could be used to recognize self-esteem with a fairly good criterion validity. The gait predicting model can be taken as a good supplementary method to measure self-esteem. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Application of pattern recognition to seismic event discrimination

    NASA Astrophysics Data System (ADS)

    Tsukada, Shin'ya; Ohtake, Kazuo

    The hypocenter determination is one of the most basic analyses in seismology. In recent years, the ability of hypocenter determination has improved rapidly. The more we try to raise the ability of automatic hypocenter determination, the more essential the discrimination of the seismic signal from the background noise becomes. Even if the technique of automatic picking or calculation of hypocenter determination is upgraded in the automatic processing, the reliability of hypocenter determination worsens when there are a lot of misreading of the phase by the noise. We propose a new approach or “a method of seismic event discrimination with pattern recognition” that determines seismic events precisely, which may serve for increasing the reliability of automatic reading of seismogram and hypocenter determination. In the current method of seismic signal discrimination, the information of seismic wave arriving at the station is positively used. However we can not say that we have explicity used the information that the seismic wave still has not arrived at the station. We try to use this information effectively. Our method will be useful for the observation of the seismic wave-field on a real time. *** DIRECT SUPPORT *** A04BD016 00010

  8. Characterization of SEM speckle pattern marking and imaging distortion by digital image correlation

    NASA Astrophysics Data System (ADS)

    Guery, Adrien; Latourte, Félix; Hild, François; Roux, Stéphane

    2014-01-01

    Surface patterning by e-beam lithography and scanning electron microscope (SEM) imaging distortions are studied via digital image correlation. The global distortions from the reference pattern, which has been numerically generated, are first quantified from a digital image correlation procedure between the (virtual) reference pattern and the actual SEM image both in secondary and backscattered electron imaging modes. These distortions result from both patterning and imaging techniques. These two contributions can be separated (without resorting to an external caliper) based on the images of the same patterned surface acquired at different orientations. Patterning distortions are much smaller than those due to imaging on wide field images.

  9. Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing

    PubMed Central

    St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B.

    2012-01-01

    There is currently no “gold standard” marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the “real world” or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26 – 52 hours. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual’s behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in

  10. Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

    PubMed Central

    Noh, Soo Rim; Isaacowitz, Derek M.

    2014-01-01

    While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

  11. Hox genes regulate digit patterning by controlling the wavelength of a Turing-type mechanism.

    PubMed

    Sheth, Rushikesh; Marcon, Luciano; Bastida, M Félix; Junco, Marisa; Quintana, Laura; Dahn, Randall; Kmita, Marie; Sharpe, James; Ros, Maria A

    2012-12-14

    The formation of repetitive structures (such as stripes) in nature is often consistent with a reaction-diffusion mechanism, or Turing model, of self-organizing systems. We used mouse genetics to analyze how digit patterning (an iterative digit/nondigit pattern) is generated. We showed that the progressive reduction in Hoxa13 and Hoxd11-Hoxd13 genes (hereafter referred to as distal Hox genes) from the Gli3-null background results in progressively more severe polydactyly, displaying thinner and densely packed digits. Combined with computer modeling, our results argue for a Turing-type mechanism underlying digit patterning, in which the dose of distal Hox genes modulates the digit period or wavelength. The phenotypic similarity with fish-fin endoskeleton patterns suggests that the pentadactyl state has been achieved through modification of an ancestral Turing-type mechanism.

  12. Hox Genes Regulate Digit Patterning by Controlling the Wavelength of a Turing-Type Mechanism

    PubMed Central

    Sheth, Rushikesh; Marcon, Luciano; Félix Bastida, M.; Junco, Marisa; Quintana, Laura; Dahn, Randall; Kmita, Marie; Sharpe, James; Ros, Maria A.

    2015-01-01

    The formation of repetitive structures (such as stripes) in nature is often consistent with a reaction-diffusion mechanism, or Turing model, of self-organizing systems. We used mouse genetics to analyze how digit patterning (an iterative digit/nondigit pattern) is generated. We showed that the progressive reduction in Hoxa13 and Hoxd11-Hoxd13 genes (hereafter referred to as distal Hox genes) from the Gli3-null background results in progressively more severe polydactyly, displaying thinner and densely packed digits. Combined with computer modeling, our results argue for a Turing-type mechanism underlying digit patterning, in which the dose of distal Hox genes modulates the digit period or wavelength. The phenotypic similarity with fish-fin endoskeleton patterns suggests that the pentadactyl state has been achieved through modification of an ancestral Turing-type mechanism. PMID:23239739

  13. A Chemical Sensor Pattern Recognition System Using a Self-Training Neural Network Classifier With Automated Outlier Detection

    DTIC Science & Technology

    1998-04-17

    A device and method for a pattern recognition system using a self-training neural network classifier with automated outlier detection for use in...chemical sensor array systems. The pattern recognition system uses a Probabilistic Neural Network (PNN) training computer system to develop automated

  14. A smart pattern recognition system for the automatic identification of aerospace acoustic sources

    NASA Technical Reports Server (NTRS)

    Cabell, R. H.; Fuller, C. R.

    1989-01-01

    An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

  15. Research on Gesture Definition and Electrode Placement in Pattern Recognition of Hand Gesture Action SEMG

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Chen, Xiang; Zhao, Zhang-Yan; Tu, You-Qiang; Yang, Ji-Hai; Lantz, Vuokko; Wang, Kong-Qiao

    The goal of this study is to explore the effects of electrode place-ment on the hand gesture pattern recognition performance. We have conducted experiments with surface EMG sensors using two detecting electrode channels. In total 25 different hand gestures and 10 different electrode positions for measuring muscle activities have been evaluated. Based on the experimental results, dependencies between surface EMG signal detection positions and hand gesture recognition performance have been analyzed and summarized as suggestions how to define hand gestures and select suitable electrode positions for a myoelectric control system. This work provides useful insight for the development of a medical rehabilitation system based on EMG technique.

  16. Aminoacyl-tRNAs from Physarum polycephalum: patterns of codon recognition.

    PubMed Central

    Hatfield, D; Rice, M; Hession, C A; Melera, P W

    1982-01-01

    Isoacceptors of Physarum polycephalum Ala-, Arg-, Glu-, Gln-, Gly-, Ile-, Leu-, Lys-, Ser-, Thr-, and Val-tRNAs were resolved by reverse-phase chromatography and isolated, and their codon recognition properties were determined in a ribosomal binding assay. Codon assignments were made to most isoacceptors, and they are summarized along with those determined in other studies from Escherichia coli, yeasts, wheat germ, hymenoptera, Xenopus, and mammals. The patterns of codon recognition by isoacceptors from P. polycephalum are more similar to those of animals than to those of plants or lower fungi. PMID:7047488

  17. Local directional pattern of phase congruency features for illumination invariant face recognition

    NASA Astrophysics Data System (ADS)

    Essa, Almabrok E.; Asari, Vijayan K.

    2014-04-01

    An illumination-robust face recognition system using Local Directional Pattern (LDP) descriptors in Phase Congruency (PC) space is proposed in this paper. The proposed Directional Pattern of Phase Congruency (DPPC) is an oriented and multi-scale local descriptor that is able to encode various patterns of face images under different lighting conditions. It is constructed by applying LDP on the oriented PC images. A LDP feature is obtained by computing the edge response values in eight directions at each pixel position and encoding them into an eight bit binary code using the relative strength magnitude of these edge responses. Phase congruency and local directional pattern have been independently used in the field of face and facial expression recognition, since they are robust to illumination changes. When the PC extracts the discontinuities in the image such as edges and corners, the LDP computes the edge response values in different directions and uses these to encode the image texture. The local directional pattern descriptor on the phase congruency image is subjected to principal component analysis (PCA) for dimensionality reduction for fast and effective face recognition application. The performance evaluation of the proposed DPPC algorithm is conducted on several publicly available databases and observed promising recognition rates. Better classification accuracy shows the superiority of the LDP descriptor against other appearance-based feature descriptors such as Local Binary Pattern (LBP). In other words, our result shows that by using the LDP descriptor the Euclidean distance between reference image and testing images in the same class is much less than that between reference image and testing images from the other classes.

  18. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

    PubMed

    Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

  19. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

    PubMed Central

    Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating. PMID:28058046

  20. Adsorption and Pattern Recognition of Polymers at Complex Surfaces with Attractive Stripelike Motifs

    NASA Astrophysics Data System (ADS)

    Möddel, Monika; Janke, Wolfhard; Bachmann, Michael

    2014-04-01

    We construct the complete structural phase diagram of polymer adsorption at substrates with attractive stripelike patterns in the parameter space spanned by the adsorption affinity of the stripes and temperature. Results were obtained by extensive generalized-ensemble Monte Carlo simulations of a generic model for the hybrid organic-inorganic system. By comparing with adhesion properties at homogeneous substrates, we find substantial differences in the formation of adsorbed polymer structures if translational invariance at the surface is broken by a regular pattern. Beside a more specific understanding of polymer adsorption processes, our results are potentially relevant for the design of macromolecular pattern recognition devices such as sensors.