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1

Optical Music Recognition: the Case Study of Pattern Recognition  

Microsoft Academic Search

The paper presents a pattern recognition study aimed on music notation recognition. The study is focused on practical aspect\\u000a of optical music recognition; it presents a variety of methods applied in optical music recognition technology. The following\\u000a logically separated stages of music notation recognition are distinguished: acquiring music notation structure, recognizing\\u000a symbols of music notation, analyzing contextual information. The directions

Wladyslaw Homenda

2005-01-01

2

Correlation, functional analysis and optical pattern recognition  

SciTech Connect

Correlation integrals have played a central role in optical pattern recognition. The success of correlation, however, has been limited. What is needed is a mathematical operation more complex than correlation. Suitably complex operations are the functionals defined on the Hilbert space of Lebesgue square integrable functions. Correlation is a linear functional of a parameter. In this paper, we develop a representation of functionals in terms of inner products or equivalently correlation functions. We also discuss the role of functionals in neutral networks. Having established a broad relation of correlation to pattern recognition, we discuss the computation of correlation functions using acousto-optics.

Dickey, F.M.; Lee, M.L.; Stalker, K.T.

1994-03-01

3

Multiple degree of freedom optical pattern recognition  

NASA Technical Reports Server (NTRS)

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.

Casasent, D.

1987-01-01

4

Comparison of Real-Time Optical Correlators for Pattern Recognition.  

National Technical Information Service (NTIS)

Two types of optical correlators have been built to investigate real-time pattern recognition. The first employs one-dimensional devices to perform the two dimensional correlation in real time. This architecture uses an array of light emitting diodes (LED...

K. T. Stalker, P. A. Molley, B. D. Hansche

1989-01-01

5

Optical pattern recognition based on color vision models  

NASA Astrophysics Data System (ADS)

A channel transformation based on opponent-color theory of the color vision models is applied to optical pattern recognition so that the conventional red, green, and blue (RGB) channels are transformed into bright-dark, red-green, and yellow-blue (ATD) channels. Matched filtering and correlation are performed over the new components of the target and the scene in the ATD system. The proposed transformation allows us to reduce the number of channels commonly used in color pattern recognition, passing from the three RGB channels to the two red-green and yellow-blue opponent-color channels.

Millán, M. S.; Corbalán, M.; Romero, J.; Yzuel, M. J.

1995-08-01

6

High Efficiency Pattern Recognition.  

National Technical Information Service (NTIS)

This contract had two main goals: the improvement and comparison of optical pattern recognition filter algorithms and; the improvement of the optical or Horner efficiency in order to allow smaller lower powered lasers for pattern recognition systems. The ...

S. Feynman S. H. Lee

1989-01-01

7

Real-valued composite filters for optical pattern recognition  

NASA Technical Reports Server (NTRS)

The design of real-valued composite filters for optical pattern recognition and classification is considered. A procedure to design a real-valued minimum average correlation energy (MACE) filter is developed. Also, the design of a real MVSDF-MACE filter that minimizes the output variance due to input noise while maintaining a sharp correlation peak is developed. Computer simulation indicates that the performance of these real filters is almost as good as that of the complex filters.

Balendra, A.; Rajan, P. K.

1993-01-01

8

A polymeric optical pattern-recognition system for security verification  

NASA Astrophysics Data System (ADS)

POLYMERS that exhibit the photorefractive effect-a light-induced modulation of refractive index-are emerging as attractive materials for optical devices and processing systems1,2. Here we demonstrate one such application using our recently developed high-efficiency photorefractive polymer2. The polymer provides a nonlinear medium in which real-time all-optical image correlation, and hence pattern recognition, can be accomplished. This forms the basis of an optical security system, whereby documents are encoded with practically invisible phase masks (such masks are difficult to forge), which may then be rapidly screened to verify the authenticity of the documents. The wavelengths at which our optical system operates are compatible with commercial low-power semiconductor laser diodes, and the system can be integrated into a compact device at low cost.

Volodin, B. L.; Kippelen, B.; Meerholz, K.; Javidi, B.; Peyghambarian, N.

1996-09-01

9

Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition  

NASA Technical Reports Server (NTRS)

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.

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

1994-01-01

10

Application of semiconductor optical amplifier logic gates in high-speed all-optical pattern recognition  

Microsoft Academic Search

In this paper, we propose and demonstrate a novel all-optical pattern recognition system. The system is able to detect and locate a specified target pattern within an input data sequence at high line-rate. The key elements of the system are an all-optical XNOR logic gate and an all-optical AND gate, the latter forming part of a recirculating loop. Both these

Xuelin Yang; Roderick P. Webb; Robert J. Manning; David Cotter; Graeme D. Maxwell; Alistair J. Poustie; Sebastien Lardenois

2008-01-01

11

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

NASA Technical Reports Server (NTRS)

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.

Chao, Tien-Hsin

1991-01-01

12

An investigation of optical composite filters for pattern recognition  

NASA Astrophysics Data System (ADS)

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 dynamic range. The simulated results show that high discrimination can be achieved among very similar tank SAR images with 360-degree rotation- invariance, by using six 9-level MCF filters. With these features and excellent performance, we anticipate that the MCF filters will play an important role in optical pattern recognition.

Li, Chun-Te

13

Summary of the transfer of optical processing to systems: optical pattern recognition program  

NASA Astrophysics Data System (ADS)

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.

Lindell, Scott D.

1995-06-01

14

Binary optical filters for scale invariant pattern recognition  

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

15

Optical pattern recognition III; Proceedings of the Meeting, Orlando, FL, Apr. 21, 22, 1992  

NASA Technical Reports Server (NTRS)

Consideration is given to transitioning of optical processing into systems (TOPS), optical correlator hardware, phase-only optical correlation filters, optical distortion-invariant correlation filters, and optical neural networks. Particular attention is given to a test target for optical correlators, a TOPS electronic warfare channelizer program, a portable video-rate optical correlator, a joint transform correlator employing electron trapping materials, a novelty filtered optical correlator using a photorefractive crystal, a comparison of correlation performance of smart ternary phase-amplitude filters with gray-scale and binary input scenes, real-time distortion-tolerant composite filters for automatic target identification, landscaping the correlation surface, fast designing of a circular harmonic filter using simulated annealing, feature-based correlation filters for distortion invariance, automatic target recognition using a feature-based optical neural network, and a holographic inner-product processor for pattern recognition.

Casasent, David P. (editor); Chao, Tien-Hsin (editor)

1992-01-01

16

Optimum and applications of photorefractive spatial light modulator in optical pattern recognition  

NASA Astrophysics Data System (ADS)

With excellent physical properties the photorefractive crystals, such as BSO (Bi12SiO20), BaTiO3 and GaAs materials, have, can be widely used in optical correlator to implement auto pattern recognition. As the basic devices in optical correlator, the properties of optically-addressed spatial light modulator are very important. By analyzing the dynamic process of the BSO spatial light modulator, especially the changes of the read-out light while in writing under various operation modes, the distinctness between various operation modes is summarize. Furthermore, considered with the photo-induced current pulses, the method to optimize the BSO spatial light modulator is proposed. The BSO spatial light modulator working in optimum operation mode is used to design a optical correlator to implement auto pattern recognition.

Li, Xiujian; Hu, Wenhua; Jia, Hui; Yang, Jiankun; Yang, Yisheng; Guo, Shaofeng

2010-04-01

17

A novel optical/digital processing system for pattern recognition  

NASA Technical Reports Server (NTRS)

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.

Boone, Bradley G.; Shukla, Oodaye B.

1993-01-01

18

Passive and active optical bit-pattern recognition structures for multiwavelength optical packet switching networks  

Microsoft Academic Search

Next generation High-Speed optical packet switching networks require components capable of recognising the optical header to enable on-the-fly accurate switching of incoming data packets to their destinations. This paper experimentally demonstrates a comparison between two different optical header recognition structures; A passive structure based on the use of Fiber Bragg Gratings (FBGs), whereas the active structure employs Opto-VLSI processors that

Muhsen Aljada; Kamal Alameh

2007-01-01

19

Distortion-invariant optical pattern recognition by correlative matrix feature criterion  

NASA Astrophysics Data System (ADS)

In this paper, we used correlative matrix as a feature criterion in K-L transformation for feature compression, and then the synthetic matched filter (SMF) was prepared by means of the synthetic discriminant function (SDF). This method can effectively reduce the size of feature image. By this fiber, the shift, scale and rotation in variant optical pattern recognition was realized with a high signal noise ratio of correlation output.

Baotang, Yang; Zhengping, Lan

1994-12-01

20

Pattern recognition principles  

NASA Technical Reports Server (NTRS)

The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems. Areas covered include decision functions, pattern classification by distance functions, pattern classification by likelihood functions, the perceptron and the potential function approaches to trainable pattern classifiers, statistical approach to trainable classifiers, pattern preprocessing and feature selection, and syntactic pattern recognition.

Tou, J. T.; Gonzalez, R. C.

1974-01-01

21

Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system  

NASA Astrophysics Data System (ADS)

In this paper we propose and demonstrate the scheme of vibration pattern recognition and classification in the OTDR based distributed optical-fiber vibration sensing system. We set up the engineering system with signal processing PC for perimeter security in some high-tech park in Nanjing. Three types of disturbing actions, including climbing up and kicking at the wall by a person, and watering on the sensing optical fiber cable same as the rain falling on, are implemented. By using level crossing rate (LCR), we can obtain their individual pattern features, so that the eigenvalue database for three disturbing actions can be built in the system. By comparing three types of vibrations, the differences among these can be given out. The results show three vibration patterns can be recognized and classified effectively.

Zhu, Hui; Pan, Chao; Sun, Xiaohan

2014-03-01

22

Advanced ultra-high-capacity optical random access memory and pattern recognition techniques  

NASA Astrophysics Data System (ADS)

The architecture and mathematical analysis of a new multi- channel multistage holographic optical random access memory (HORAM) architecture and an experimental demonstration of its feasibility are presented. The new HORAM can be used for ultra-high-capacity storage and high-speed random retrieval of information. A two-stage HORAM, using a Dammann grating and a multifocus holographic lens, clearly shows the capability of storing 2000 holographic matched filters. The functional requirements for key optical elements including laser sources, spatial light modulators, and electro-optical shutters for making a desired practical and compact HORAM are described. The potential extension of the HORAM system for multiple-channel optical pattern recognition, classification, and image restoration are described.

Liu, Hua-Kuang; Jin, Yahong; Marzwell, Neville I.

1998-03-01

23

A high-speed readout scheme for fast optical correlation-based pattern recognition  

NASA Astrophysics Data System (ADS)

We describe recent developments to a novel form of hybrid electronic/photonic correlator, which exploits component innovations in both electronics and photonics to provide fast, compact and rugged target recognition, applicable to a wide range of security applications. The system benefits from a low power, low volume, optical processing core which has the potential to realise man portable pattern recognition for a wide range of security based imagery and target databases. In the seminal Vander Lugt correlator the input image is Fourier transformed optically and multiplied optically with the conjugate Fourier transform of a reference pattern; the required correlation function is completed by taking the inverse Fourier transform of the product optically. The correlator described here is similar in principle, but performs the initial Fourier transforms and multiplication electronically, with only the final most computationally demanding output Fourier transform being performed optically. In this scheme the Fourier transforms of both the input scene and reference pattern are reduced to a binary phase-only format, where the multiplication process simplifies to a simple Boolean logic XOR function. The output of this XOR gate is displayed on a state-of-the-art Fast Bit Plane Spatial Light Modulator (FBPSLM). A novel readout scheme has been developed which overcomes the previous system output bottleneck and for the first time allows correlation frame readout rates capable of matching the inherently fast nature of the SLM. Readout rates of up to ~1 MHz are now possible, exceeding current SLM capabilities and meeting potential medium term SLM developments promised by SLMs based on novel materials and architectures.

McDonald, Gregor J.; Lewis, Meirion F.; Wilson, Rebecca

2004-12-01

24

Real-valued composite filters for correlation-based optical pattern recognition  

NASA Technical Reports Server (NTRS)

Advances in the technology of optical devices such as spatial light modulators (SLMs) have influenced the research and growth of optical pattern recognition. In the research leading to this report, the design of real-valued composite filters that can be implemented using currently available SLMs for optical pattern recognition and classification was investigated. The design of real-valued minimum average correlation energy (RMACE) filter was investigated. Proper selection of the phase of the output response was shown to reduce the correlation energy. The performance of the filter was evaluated using computer simulations and compared with the complex filters. It was found that the performance degraded only slightly. Continuing the above investigation, the design of a real filter that minimizes the output correlation energy and the output variance due to noise was developed. Simulation studies showed that this filter had better tolerance to distortion and noise compared to that of the RMACE filter. Finally, the space domain design of RMACE filter was developed and implemented on the computer. It was found that the sharpness of the correlation peak was slightly reduced but the filter design was more computationally efficient than the complex filter.

Rajan, P. K.; Balendra, Anushia

1992-01-01

25

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

NASA Astrophysics Data System (ADS)

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.

Starikov, Sergey N.; Konnik, Mikhail V.

2008-03-01

26

Design of coupled mace filters for optical pattern recognition using practical spatial light modulators  

NASA Technical Reports Server (NTRS)

Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter.

Rajan, P. K.; Khan, Ajmal

1993-01-01

27

Smart pattern recognition  

NASA Astrophysics Data System (ADS)

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.

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

2013-03-01

28

A comparison of real-time optical correlators for pattern recognition  

SciTech Connect

Two types of optical correlators have been built to investigate real-time pattern recognition. The first employs one-dimensional devices to perform the two dimensional correlation in real time. This architecture uses an array of light emitting diodes (LED's) to input an electronically stored reference image into the processor in parallel. The input scene data is introduced into the processor one line at a time using an acousto-optic device (AOD). Multichannel time integrating correlations are performed in the row direction using the AOD and in the column direction using a charge coupled device (CCD) operating in the time delay and integrate mode. a processor has been built using this technology which correlates a 64 /times/ 44 pixel binary reference image with a 256 /times/ 232 input scene at video rates. The second correlator is a space integrating Fourier transform based correlator. A magneto optic-device (MOD) is used at the Fourier transform plane to rapidly change filter functions. The binary nature of the MOD device necessitates using either a binary phase or binary amplitude representation of the desired complex filter function. For this reason, several types of Binary Phase-Only Filter (BPOF) representations have been analyzed and experimentally investigated. Experimental correlation results have been obtained using both the Hartley BPOF and a newly developed class of complex binary filters, called Quad-Phase-Only Filters (QPOF). The performance of the two systems will be compared on the basis of processing speed, space bandwidth product, processor size and light efficiency. The inherent differences between incoherent and coherent processing and their implications for filter design will also be discussed. Finally, estimates of future performance will be presented. 10 refs., 8 figs., 2 tabs.

Stalker, K.T.; Molley, P.A.; Hansche, B.D.

1989-01-01

29

A comparison of real-time optical correlators for pattern recognition  

NASA Astrophysics Data System (ADS)

Two types of optical correlators were built to investigate real time pattern recognition. The first employs 1-D devices to perform the two dimensional correlation in real time. This architecture uses an array of light emitting diodes (LED's) to input an electronically stored reference image into the processor in parallel. The input scene data is introduced into the processor one line at a time using an acousto-optic device (AOD). Multichannel time integrating correlations are performed in the row direction using the AOD and in the column direction using a charge coupled device (CCD) operating in the time delay and integrate mode. A processor was built using this technology which correlates a 64 x 44 pixel binary reference image with a 256 x 232 input scene at video rates. The second correlator is a space integrating Fourier transform based correlator. A magneto optic-device (MOD) is used at the Fourier transform plane to rapidly change filter functions. The binary nature of the MOD device necessitates using either a binary phase or binary amplitude representation of the desired complex filter function. For this reason, several types of Binary Phase-Only Filter (BPOF) representations were analyzed and experimentally investigated. Experimental correlation results were obtained using both the Hartley BPOF and a newly developed class of complex binary filters, called Quad-Phase-Only Filters (QPOF). The performance of the two systems are compared on the basis of processing speed, space bandwidth product, processor size and light efficiency. The inherent differences between incoherent and coherent processing and their implications for filter design are also discussed. Finally, estimates of future performance are presented.

Stalker, K. Terry; Molley, Perry A.; Hansche, Bruce D.

30

Log-Polar Optical Coordinate Transformation with Applications for Automatic Pattern Recognition.  

National Technical Information Service (NTIS)

An important task in machine vision and target recognition is the rapid estimation of the orientation and size of an object with respect to a reference. An optical image processing system is described which converts in-plane rotation and size variations i...

C. W. Keefer M. A. Getbehead W. E. Foor

1994-01-01

31

Optical Character Recognition System for Urdu (Naskh Font) Using Pattern Matching Technique  

Microsoft Academic Search

The offline optical character recognition (OCR) for different languages has been developed over the recent years. Since 1965, the US postal service has been using this system for automating their services. The range of the applications under this area is increasing day by day, due to its utility in almost major areas of government as well as private sector. This

Tabassam Nawaz; Syed Ammar; Hassan Shah

32

Pattern recognition in bioinformatics.  

PubMed

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. PMID:23559637

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

2013-09-01

33

Anthropomorphic pattern formation and recognition systems  

NASA Astrophysics Data System (ADS)

In first part the Optical Pattern Formation system, i.e. the non-traditional Super-Scanning Locator, is shortly described. The goal of the second part--creation the Optical Pattern Recognition system working alike the Natural one, where receiving information, before reach the natural computer (brain), is pre-processed by the 5 organs of sense, mostly by analogous methods.

Ginzburg, Vera M.

2000-03-01

34

Pattern Recognition and Image Processing  

Microsoft Academic Search

Extensive research and development has taken place over the last 20 years in the areas of pattern recognition and image processing. Areas to which these disciplines have been applied include business (e. g., character recognition), medicine (diagnosis, abnormality detection), automation (robot vision), military intelligence, communications (data compression, speech recognition), and many others. This paper presents a very brief survey of

King-sun Fu; Azriel Rosenfeld

1976-01-01

35

Pattern recognition and neural networks  

Microsoft Academic Search

ashing powders (Carstensen, 1992).In all these of tasks there is a predefined set of classes of patterns which might bepresented, and the task is to classify a future pattern as one of these classes. Suchtasks are called classification or supervised pattern recognition1. Clearly someonehad to determine the classes in the first place, and seeking groupings of patterns iscalled cluster analysis

Brian D. Ripley

1996-01-01

36

Pattern Recognition of Logic Diagrams.  

National Technical Information Service (NTIS)

This paper presents the development of line following and line analysis pattern recognition routines to automatically computer read hand-drawn logic diagrams. The decision criterion used was based on a tree analysis of segmented lines. The logic diagrams ...

D. A. Naegele

1973-01-01

37

Pattern Recognition from Satellite Altitudes  

Microsoft Academic Search

Several decision algorithms were used to classify complex patterns recorded by TV cameras aboard unmanned, scientific satellites. Recognition experiments were performed with two kinds of patterns: lunar topographic features and clouds in the earth's atmosphere. Classification accuracies ranged from 53 percent to 99 percent on independent data.

Eugene Darling; R. D. Joseph

1968-01-01

38

Linear methods for input scenes restoration from signals of optical-digital pattern recognition correlator  

NASA Astrophysics Data System (ADS)

Linear methods of restoration of input scene's images in optical-digital correlators are described. Relatively low signal to noise ratio of a camera's photo sensor and extensional PSF's size are special features of considered optical-digital correlator. RAW-files of real correlation signals obtained by digital photo sensor were used for input scene's images restoration. It is shown that modified evolution method, which employs regularization by Tikhonov, is better among linear deconvolution methods. As a regularization term, an inverse signal to noise ratio as a function of spatial frequencies was used. For additional improvement of restoration's quality, noise analysis of boundary areas of the image to be reconstructed was performed. Experimental results on digital restoration of input scene's images are presented.

Starikov, Sergey N.; Konnik, Mikhail V.; Manykin, Edward A.; Rodin, Vladislav G.

2009-04-01

39

Optical pattern recognition architecture implementing the mean-square error correlation algorithm  

DOEpatents

An optical architecture implementing the mean-square error correlation algorithm, MSE=.SIGMA.[I-R].sup.2 for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s.sub.1 (t) and a time-varying input image signal s.sub.2 (t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I.sub.1 (t) having the form I.sub.1 (t)=A.sub.1 [1+.sqroot.2m.sub.1 s.sub.1 (t)cos (2.pi.f.sub.o t)] and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by: I.sub.2 (t)=A.sub.2 [+2m.sub.2.sup.2 s.sub.2.sup.2 (t)-2.sqroot.2m.sub.2 (t) cos (2.pi.f.sub.o t] The time integration of the two signals I.sub.1 (t) and I.sub.2 (t) on the CCD deflector plane produces the result R(.tau.) of the mean-square error having the form: R(.tau.)=A.sub.1 A.sub.2 {[T]+[2m.sub.2.sup.2.multidot..intg.s.sub.2.sup.2 (t-.tau.)dt]-[2m.sub.1 m.sub.2 cos (2.tau.f.sub.o .tau.).multidot..intg.s.sub.1 (t)s.sub.2 (t-.tau.)dt]} where: s.sub.1 (t) is the signal input to the diode modulation source: s.sub.2 (t) is the signal input to the AOD modulation source; A.sub.1 is the light intensity; A.sub.2 is the diffraction efficiency; m.sub.1 and m.sub.2 are constants that determine the signal-to-bias ratio; f.sub.o is the frequency offset between the oscillator at f.sub.c and the modulation at f.sub.c +f.sub.o ; and a.sub.o and a.sub.1 are constant chosen to bias the diode source and the acousto-optic deflector into their respective linear operating regions so that the diode source exhibits a linear intensity characteristic and the AOD exhibits a linear amplitude characteristic.

Molley, Perry A. (Albuquerque, NM)

1991-01-01

40

Pattern recognition of geophysical data  

Microsoft Academic Search

A new rock classification method for ground penetrating radar (GPR) data is presented for cases where no additional geological information is available from boreholes. There are non-linear relationships between petrophysical properties of rocks and electromagnetic waves which can be handled using two methods derived from statistical learning theory on pattern recognition. An investigation was carried out looking at proving the

Bernd Ehret

2010-01-01

41

Neural Networks for Pattern Recognition  

Microsoft Academic Search

his is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization,

Christopher M. Bishop

1995-01-01

42

Massive parallel optical pattern recognition and retrieval via a two-stage high-capacity multichannel holographic random access memory system  

NASA Astrophysics Data System (ADS)

The multistage holographic optical random access memory (HORAM) system reported recently by Liu et al. provides a new degree of freedom for improving storage capacity. We further present a theoretical and practical analysis of the HORAM system with experimental results. Our discussions include the system design and geometrical requirements, its applications for multichannel pattern recognition and associative memory, the 2D and 3D information storage capacity, and multichannel image storage and retrieval via VanderLugt correlator (VLC) filters and joint transform holograms. A series of experiments are performed to demonstrate the feasibility of the multichannel pattern recognition and image retrieval with both the VLC and joint transform correlator architectures. The experimental results with as many as 2025 channels show good agreement with the theoretical analysis.

Cai, Luzhong; Liu, Hua-Kuang

2000-02-01

43

A real-time optical automatic target recognition system  

Microsoft Academic Search

Automatic target recognition (ATR) technique has been applied in both civil and military. In this paper, we present a new optical pattern recognition system for target recognition. This system includes synthetic discriminate function (SDF) based practical optimized filters for the 3-D targets, the Reference Filter Libs for high correlation SNR, the mapping between the input (object regions) and the output

Huaixin Chen; Jianshe Nan; Xiaosun Li; Honggang Wei

2004-01-01

44

Pattern Recognition Receptors and Autophagy  

PubMed Central

The immune system senses exogenous threats or endogenous stress through specialized machinery known as pattern recognition receptors (PRRs). These receptors recognize conserved molecular structures and initiate downstream signaling pathways to control immune responses. Although various immunologic pathways mediated by PRRs have been described, recent studies have demonstrated a link between PRRs and autophagy. Autophagy is a specialized biological process involved in maintaining homeostasis through the degradation of long-lived cellular proteins and organelles. In addition to this fundamental function, autophagy plays important roles in various immunologic processes. In this review, we focus on the reciprocal influences of PRRs and autophagy in modulating innate immune responses.

Oh, Ji Eun; Lee, Heung Kyu

2014-01-01

45

Deterministic Learning and Rapid Dynamical Pattern Recognition  

Microsoft Academic Search

Recognition of temporal\\/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns. First, it is shown that a time-varying dynamical pattern can be effectively represented in a time-invariant and spatially distributed manner through deterministic learning. Second, a

Cong Wang; David J. Hill

2007-01-01

46

Nonparametric and Linguistic Approaches to Pattern Recognition.  

National Technical Information Service (NTIS)

The report investigates two approaches to pattern recognition which utilize information about pattern organization. First, a nonparametric method is developed for estimating the probability density functions associated with the pattern classes. The disper...

P. H. Swain K. S. Fu

1970-01-01

47

Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition  

Microsoft Academic Search

A discriminant function-based minimum recognition error rate pattern recognition approach is described and studied for various applications in speech processing. This approach departs from the conventional paradigm, which links a classification\\/recognition task to the problem of distribution estimation. Instead, it takes a discriminant function based statistical pattern recognition approach. The suitability of this approach for classification error rate minimization is

Wu Chou

2000-01-01

48

Adaptive pattern recognition and neural networks  

Microsoft Academic Search

The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set approach, patterns with nonnumeric feature values, learning discriminants and the generalized perceptron, recognition and recall on the basis of partial cues, associative memories,

Yoh-Han Pao; Yohhan

1989-01-01

49

Pattern recognition systems and procedures  

NASA Technical Reports Server (NTRS)

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.

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

1972-01-01

50

Inverse scattering approach to improving pattern recognition  

NASA Astrophysics Data System (ADS)

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.

Chapline, George; Fu, Chi-Yung

2005-05-01

51

Experiences in Pattern Recognition for Machine Olfaction  

Microsoft Academic Search

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights.

C. Bessant

2011-01-01

52

Pattern recognition for statistical process control charts  

Microsoft Academic Search

Control charts are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Patterns displayed on control charts can provide information about the process. This paper describes the development of a pattern recognition system designed to detect and analyse various patterns that can occur on statistical quality

Jiemin Wang; A. K. Kochhar; R. G. Hannam

1998-01-01

53

On musical stylometry - a pattern recognition approac  

Microsoft Academic Search

In this short communication we describe some experiments in which methods of statistical pattern recognition are applied for musical style recognition and disputed musical authorship attribution.Values of a set of 20 features (also called “style markers”) are measured in the scores of a set of compositions, mainly describing the different sonorities in the compositions. For a first study over 300

Eric Backer; Peter Van Kranenburg

2005-01-01

54

Pattern Recognition Applied to Cough Categorization.  

National Technical Information Service (NTIS)

The particular problem with which the research was concerned was the development of a technique to discriminate between coughs and other audible phenomena which originate in a hospital environment. Pattern recognition provided such a technique. Experiment...

J. L. Devine A. J. Welch

1967-01-01

55

Pattern Recognition by Retina-Like Devices.  

National Technical Information Service (NTIS)

The 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 ...

C. F. R. Weiman J. Rothstein

1972-01-01

56

Visual cluster analysis and pattern recognition methods  

DOEpatents

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.

Osbourn, Gordon Cecil (Albuquerque, NM); Martinez, Rubel Francisco (Albuquerque, NM)

2001-01-01

57

Adaptive pattern recognition and neural networks  

SciTech Connect

The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set approach, patterns with nonnumeric feature values, learning discriminants and the generalized perceptron, recognition and recall on the basis of partial cues, associative memories, self-organizing nets, the functional-link net, fuzzy logic in the linking of symbolic and subsymbolic processing, and adaptive pattern recognition and its applications. Also included are C-language programs for (1) a generalized delta-rule net for supervised learning and (2) unsupervised learning based on the discovery of clustered structure. 183 refs.

Pao, Yohhan.

1989-01-01

58

Optical Character Recognition System for Personal Computers.  

National Technical Information Service (NTIS)

The paper describes the outline of an optical character recognition (OCR) system using the Panacom M series personal computers, and its character recognition algorithm. The algorithm can be adapted to type characters and characters written by a specific p...

M. Sugimoto Y. Shiwaku A. Fujiwara M. Takenouchi T. Yokoe

1989-01-01

59

Pattern recognition, inner products and correlation filters  

SciTech Connect

In this paper, we review correlation filters as an approach to pattern recognition with a special emphasis on the consequences of normalizing the correlation to achieve intensity invariance. Intensity invariance is effected using the Cauchy-Schwarz inequality to normalize the correlation integral. We discuss the implications of this criterion for the application of correlation filters to the pattern recognition problem. It is shown that normalized phase-only and synthetic discriminate functions do not provide the recognition/discrimination obtained with the classical matched filter. 34 refs., 5 figs.

Dickey, F.M.; Romero, L.A.

1991-01-01

60

Public domain optical character recognition  

NASA Astrophysics Data System (ADS)

A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The system's source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition system's software architecture, including descriptions of the various system components along with timing and accuracy statistics.

Garris, Michael D.; Blue, James L.; Candela, Gerald T.; Dimmick, Darrin L.; Geist, Jon C.; Grother, Patrick J.; Janet, Stanley A.; Wilson, Charles L.

1995-03-01

61

Pattern recognition with weighted complex networks  

NASA Astrophysics Data System (ADS)

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Cheh, Jigger; Zhao, Hong

2008-11-01

62

Normalized correlation for pattern recognition  

Microsoft Academic Search

The normalization of the correlation filter response effects intensity invariance. We discuss the implications of a normalization based on the Cauchy--Schwarz inequality for the discrimination problem. It is shown that normalized phase-only and synthetic discriminant functions do not provide the discrimination\\/recognition obtained with the classical matched filter.

Fred M. Dickey; Louis A. Romero

1991-01-01

63

Large-memory real-time multichannel multiplexed pattern recognition  

NASA Technical Reports Server (NTRS)

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.

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

1984-01-01

64

Pattern-Recognition Processor Using Holographic Photopolymer  

NASA Technical Reports Server (NTRS)

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-law detector for the purpose of holographic recording of interference fringes. A typical state-of-the-art CCD has a pixel-pitch limited resolution of about 100 lines/mm. In contrast, the holographic photopolymer to be used in the proposed JTOC offers a resolution > 2,000 lines/mm. In addition to being disadvantageous in itself, the low resolution of the CCD causes overlap of a DC term and the desired correlation term in the output image. This overlap severely limits the correlation signal-to-noise ratio. The two-stage nature of the process limits the achievable throughput rate. A further limit is imposed by the low frame rate (typical video rates) of low- and medium-cost commercial CCDs.

Chao, Tien-Hsin; Cammack, Kevin

2006-01-01

65

Optical correlation recognition based on LCOS  

NASA Astrophysics Data System (ADS)

Vander-Lugt correlator[1] plays an important role in optical pattern recognition due to the characteristics of accurate positioning and high signal-to-noise ratio. The ideal Vander-Lugt correlator should have the ability of outputting strong and sharp correlation peak in allusion to the true target, in the existing Spatial Light Modulators[2], Liquid Crystal On Silicon(LCOS) has been the most competitive candidate for the matched filter owing to the continuous phase modulation peculiarity. Allowing for the distortions of the target to be identified including rotations, scaling changes, perspective changes, which can severely impact the correlation recognition results, herein, we present a modified Vander-Lugt correlator based on the LCOS by means of applying an iterative algorithm to the design of the filter so that the correlator can invariant to the distortions while maintaining good performance. The results of numerical simulation demonstrate that the filter could get the similar recognition results for all the training images. And the experiment shows that the modified correlator achieves the 180° rotating tolerance significantly improving the recognition efficiency of the correlator.

Tang, Mingchuan; Wu, Jianhong

2013-08-01

66

Interpolating vectors for robust pattern recognition.  

PubMed

This paper proposes a powerful algorithm for pattern recognition, which uses interpolating vectors for classifying patterns. Labeled reference vectors in a multi-dimensional feature space are first produced by a kind of competitive learning. We then assume a situation where virtual vectors, called interpolating vectors, are densely placed along line segments connecting all pairs of reference vectors of the same label. From these interpolating vectors, we choose the one that has the largest similarity to the test vector. Its label shows the result of pattern recognition. In practice, we can get the same result with a simpler process. We applied this method to the neocognitron for handwritten digit recognition and reduced the error rate from 1.52% to 1.02% for a blind test set of 5000 digits. PMID:17714913

Fukushima, Kunihiko

2007-10-01

67

Synaptic Pattern Formation During Cellular Recognition  

NASA Astrophysics Data System (ADS)

Cell-cell recognition processes often require the formation of a patterned collection of proteins and receptors (a synapse) in the intercellular junction. We examine interactions of populations of mobile receptors and ligands in apposing fluctuating membranes. This membrane constrained reaction-diffusion interaction can lead to spontaneous formation of complex patterns. Direct comparison of such spontaneously arising patterns is made with recent experimental observations of immunological synapse formation in living cells. When the protein and membrane characteristics are chosen to be those measured for the cellular environment, the length and time scales characterizing spontaneous pattern formation are in nearly quantitative agreement with these observations. Our findings suggest that cell-cell recognition occurs in an environment where natural coupling between the available forces modulates receptor-ligand binding such that the essential characteristics of synaptic patterns can emerge spontaneously. Active cellular interventions are superimposed on these self-organizing tendencies.

Groves, Jay T.; Qi, S. Y.; Chakraborty, Arup

2001-03-01

68

An optical fibre ethanol concentration sensor utilizing Fourier transform signal processing analysis and artificial neural network pattern recognition  

NASA Astrophysics Data System (ADS)

An optical fibre sensor, which is capable of detecting varying percentages of ethanol in water, is reported. In order to maximize sensitivity, a U-bend configuration was used for the sensor where the cladding was removed and the core exposed directly to the fluid under test. The sensor was interrogated using optical time domain reflectometry (OTDR). OTDR is chosen as it is a recognized technique for the interrogation of distributed multipoint sensors and it is intended to extend this work to multiple sensors on a single fibre in the future. In this investigation the sensor was exposed to 12.5, 25 and 50% ethanol and distilled water. The signal processing technique has been designed to optimize the neural network adopted in the existing sensor system. In this investigation a discrete Fourier transform, using a fast Fourier transform algorithm, is chosen and its application leads to an improvement in efficiency of the neural network, i.e. reducing the number of input and hidden layer nodes required by the artificial neural network. Using a Stuttgart neural network simulator, a feed-forward three-layer neural network was constructed with the aim of successfully classifying the sensor test conditions based on the frequency domain response of the sensor.

King, D.; Lyons, W. B.; Flanagan, C.; Lewis, E.

2003-07-01

69

Pattern Recognition, 4th Ed  

Microsoft Academic Search

an image, a face, a sound, atrend in curve, ... . Usually, there is some preprocessing to be done on this object(the feature extraction) so that one object is represented by a discrete number56 CHAPTER 2. PATTERN RECOGNITIONof values: the features, usually seen as elements of a feature vector. If from eachpattern d features are extracted, the corresponding feature vector

Sergios Theodoridis; Konstantinos Koutroumbas

2009-01-01

70

Algorithms for adaptive nonlinear pattern recognition  

NASA Astrophysics Data System (ADS)

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral remote sensing and target recognition applications. The authors' recent research in the development of adaptive TNE and adaptive LAMs is overviewed, with experimental results that show utility for a wide variety of pattern classification applications. Performance results are presented in terms of measured computational cost, noise tolerance, and classification accuracy.

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

71

Approximating matrix multiplication for pattern recognition tasks  

Microsoft Academic Search

Many pattern recognition tasks, including estimation,classification, and the finding of similar objects, makeuse of linear models. The fundamental operation in suchtasks is the computation of the dot product between aquery vector and a large database of instance vectors.Often we are interested primarily in those instance vectorswhich have high dot products with the query. Wepresent a random sampling based algorithm that

Edith Cohen; David D. Lewis

1997-01-01

72

Pattern Recognition Techniques for Radar Aircraft Identification.  

National Technical Information Service (NTIS)

Some problems concerning the design and evaluation of a pattern recognition system for low-frequency radar aircraft identification are investigated. A decision-theoretic model that includes a model of noise perturbations of the feature-vector, and a model...

S. N. ihari L. J. White

1975-01-01

73

Conformal Predictions in Multimedia Pattern Recognition  

ERIC Educational Resources Information Center

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…

Nallure Balasubramanian, Vineeth

2010-01-01

74

Application of pattern recognition methods in astronomy  

Microsoft Academic Search

The production of speckle images in astronomical observations is discussed and methods to eliminate the atmospheric transmission function are presented. Ways of resolving binary stars using pattern recognition, without knowing the transfer function, are shown. Speckle interferometry is first used to obtain the separation and angle of sight, and a learning decision algorithm is used to determine the luminosity relation.

C. Ihrig

1984-01-01

75

Improved Pattern Recognition with Artificial Clonal Selection?  

Microsoft Academic Search

In this paper, we examine the clonal selection algorithm CLONALG and the suggestion that it is suitable for pattern recogni- tion. CLONALG is tested over a series of binary character recognition tasks and its performance compared to a set of basic binary matching algorithms. A number of enhancements are made to the algorithm to im- prove its performance and the

Jennifer A. White; Simon M. Garrett

2003-01-01

76

Pattern Recognition by Retina-Like Devices.  

ERIC Educational Resources Information Center

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…

Weiman, Carl F. R.; Rothstein, Jerome

77

Pattern recognition monitoring of PEM fuel cell  

DOEpatents

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.

Meltser, M.A.

1999-08-31

78

Pattern Recognition Training for Combat Leaders: Sample Training Package.  

National Technical Information Service (NTIS)

Lesson plans were developed to facilitate and increase pattern recognition for combat leaders. Following an overview of the pattern recognition approach to training, this report contains a demonstration package that illustrates implementation of the patte...

B. D. Sullivan C. Thronesbery R. Rhoads

1996-01-01

79

On Linguistic, Statistical, and Mixed Models for Pattern Recognition.  

National Technical Information Service (NTIS)

The report presents a selective discussion of some aspects of linguistic, statistical and mixed approaches to pattern recognition, and considers the potential of transformational grammars, a proposed formalism for pattern analysis and recognition, and heu...

B. Chandrasekaran L. Kanal

1971-01-01

80

Statistical Pattern Recognition Techniques as Applied to Radar Returns.  

National Technical Information Service (NTIS)

This report presents a summary of the basic principles of pattern recognition and statistical decision theory and applies them to the problem of classifying radar returns. While pattern recognition techniques have been applied to radar signal detection pr...

W. A. Fordon A. A. Fraser

1981-01-01

81

A Pattern Recognition System based on Computer Vision - The Method of Chinese Chess Recognition  

Microsoft Academic Search

A Chinese chessman pattern recognition system is presented. First, system structure is introduced, and chessman image pretreatment is discussed. Then primarily discusses the chessman recognition algorithms. Because of direction haphazardry, chessman recognition is quite different from character recognition. This paper presents two kinds of chessman recognition algorithms - RC and concentric circle algorithm for feature extraction. Then choose a relatively

Huasheng Zhu; Jiner Lei; Xiumei Tian

2008-01-01

82

Color pattern recognition with circular component whitening  

NASA Astrophysics Data System (ADS)

Polychromatic object recognition based on circular whitening preprocessing of red-green-blue components and multichannel matched filtering is described. Computer simulations and experimental results are provided to facilitate recognizing a color target among objects of similar shape but with different color contents. Experimental results are obtained with an optical correlator with two spatial light modulators, one to introduce the scene and the second one to introduce the filter.

Moreno, I.; Kober, V.; Lashin, V.; Campos, J.; Yaroslavsky, L. P.; Yzuel, M. J.

1996-04-01

83

Discussion of problems in pattern recognition  

Microsoft Academic Search

Various problems encountered in pattern recognition were examined by an eight-man panel during the Thursday afternoon session. The panel included O. G. Selfridge, M.I.T. Lincoln Laboratory, Session Chairman; R. A. Kirsch, National Bureau of Standards; M. Minsky, Massachusetts Institute of Technology; U. Neisser, Brandeis University; and the authors of the four preceding papers --- R. J. Evey, I.B.M. Corporation, \\

W. W. Bledsoe; J. S. Bomba; I. Browning; R. J. Evey; R. A. Kirsch; R. L. Mattson; M. Minsky; U. Neisser; O. G. Selfridge

1959-01-01

84

Formal methods in pattern recognition: A review  

Microsoft Academic Search

There is lot of excitement with Pattern Recognition methods with high precision, since this problem area is a well-established field of Operations Research (O.R.). Recent work of some researchers has shown that O.R. methods in general and Optimisation methods in particular, can be applied to give some very good results. Thus this research area has been won back from the

Luciano Nieddu; Giacomo Patrizi

2000-01-01

85

VLSI Microsystem for Rapid Bioinformatic Pattern Recognition  

NASA Technical Reports Server (NTRS)

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).

Fang, Wai-Chi; Lue, Jaw-Chyng

2009-01-01

86

All-optical multibit address recognition at 20 Gb/s based on TOAD  

NASA Astrophysics Data System (ADS)

All-optical multibit address recognition at 20 Gb/s is demonstrated based on a special AND logic of terahertz optical asymmetric demultiplexer (TOAD). The semiconductor optical amplifier (SOA) used in the TOAD is biased at transparency status to accelerate the gain recovery. This is the highest bit rate that multibit address recognition is demonstrated with SOA-based interferometer. The experimental results show low pattern dependency. With this method, address recognition can be performed without separating address and payload beforehand.

Yan, Yumei; Wu, Jian; Lin, Jintong

2005-04-01

87

Developing Signal-Pattern-Recognition Programs  

NASA Technical Reports Server (NTRS)

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.

Shelton, Robert O.; Hammen, David

2006-01-01

88

Traffic signs recognition with using optical correlator  

Microsoft Academic Search

Paper presents experiments and results with traffic sing recognition system based on optical correlator. The traffic scene is captured with color camera and key frame is extracted as input for the system. The system consists of two main blocks, Preprocessing and Optical Corelator. The Cambridge Correlator was used. The ROI is defined and chosen in preprocessing block. Preprocessed ROI go

T. Harasthy; J. Turan; E. Ovsenik; K. Fazekas

2011-01-01

89

Novel video processing schemes integrating image compression and pattern recognition  

Microsoft Academic Search

In comparison to present security applications, pattern recognition techniques can be categorized as 'hard' automatic target recognition (ATR) and 'soft' ATR. The first category has been established for years and deals with specific object recognition. On the other hand, the second, less established category operates on very fast object class-level recognition only. The second category usually employs very fast processing

Igor V. Ternovskiy; Tomasz Jannson

1997-01-01

90

Pattern recognition and control in manipulation  

NASA Technical Reports Server (NTRS)

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.

Bejczy, A. K.; Tomovic, R.

1976-01-01

91

Pattern-recognition software for plant surveillance  

SciTech Connect

A power plant in steady-state operation has almost all plant signals in definite relations to each other, and a new approach to plant surveillance, which compares the patterns representing these relations, has been used to measure the consistency of many plant signals simultaneously and so derive a measure of the validity of each individual signal. In the application of this process, the real plant is used as a model from which to learn the pattern relations, and the surveillance function compares learned signal values with currently observed signal values by using contrast-enhancing, pattern-recognition algorithms. System state analyzer (SSA) software embodying this technique has been developed for the daily surveillance of the Experimental Breeder Reactor-II (EBR-II) at the Argonne National Laboratory-West site.

Mott, J.; Young, J.; Bertch, W.; King, R.W.

1987-01-01

92

Control chart pattern recognition using learning vector quantization networks  

Microsoft Academic Search

Pattern recognition systems using neural networks for discriminating between different types of control chart patterns are discussed. A class of pattern recognizers based on the Learning Vector Quantization (LVQ) network is described. A procedure to increase the classification accuracy and decrease the learning time for LVQ networks is presented. The results of control chart pattern recognition experiments using both existing

D. T. PHAM; E. OZTEMEL

1994-01-01

93

Color pattern recognition using fast quaternion correlation algorithm  

Microsoft Academic Search

Quaternion correlation techniques take advantage of high recognition precision and strong noise tolerance have been widely used in mangy application of color image processing. In this paper, a fast color pattern recognition algorithm based on quaternion correlation techniques is proposed. By traditional complex fast Fourier transform, a more efficient quaternion correlation algorithm was applied in the color pattern recognition aim

Juntao Pan; Shumin Fei; Yudong Ni; Wenlin Zou

2010-01-01

94

Opto-VLSI-based correlator architecture for multiwavelength optical header recognition  

Microsoft Academic Search

A novel optical correlator employing an opto-very-large-scale-integration (VLSI) processor to construct the routing lookup table, in conjunction with an array of fiber Bragg gratings (FBGs) for multiwavelength optical header recognition is demonstrated. The FBG array provides wavelength-dependent time delays, whereas the opto-VLSI processor generates wavelength intensity profiles that match arbitrary bit patterns. The recognition of 4-b optical patterns is experimentally

Muhsen Aljada; Kamal E. Alameh; Khalid Al-Begain

2006-01-01

95

Pattern recognition for electric power system protection  

NASA Astrophysics Data System (ADS)

The objective of this research is to demonstrate pattern recognition tools such as decision trees (DTs) and neural networks that will improve and automate the design of relay protection functions in electric power systems. Protection functions that will benefit from the research include relay algorithms for high voltage transformer protection (TP) and for high impedance fault (HIF) detection. A methodology, which uses DTs and wavelet analysis to distinguish transformer internal faults from other conditions that are easily mistaken for internal faults, has been developed. Also, a DT based solution is proposed to discriminate HIFs from normal operations that may confuse relays. Both methods have been verified with simulation data generated by the Electromagnetic Transients Program. Compared with traditional methods, both show better performance. After being trained with a large number of carefully selected features, the desired DTs can obtain an accuracy of greater than 95%. Further, no special equipment is necessary; the DT-based controller only needs the standard relay input signals sampled at 1920 Hz. So far, no one has applied the same methodologies to solve these problems. Even though some future work with experimental data is needed to make the methods more convincing for utilities, the research has already shown that pattern recognition is a promising direction in developing power system protection algorithms.

Sheng, Yong

2002-11-01

96

Nuclear Research Reactors Accidents Pattern Recognition Using Artificial Neural Networks  

Microsoft Academic Search

The patterns recognition of measured quantities for the diagnostic purposes in the field of nuclear research reactors is very important. It represents one of the fundamental tasks for the operation and accidents management. In this paper, the Nuclear Research Reactors accident's pattern recognition is tackled within neural network approach. Such patterns are introduced initially without noise. The simulated output values

Abdelfattah A. Ahmed; Nwal Ahmed Alfishawy; Mohamed A. Albrdini; Imbaby I. Mahmoud

2011-01-01

97

A neural network for visual pattern recognition  

SciTech Connect

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.

Fukushima, K.

1988-03-01

98

Application of pattern recognition for Farsi license plate recognition  

Microsoft Academic Search

A Farsi License Plate Recognition (LPR) System is one kind of automatic inspection of transport systems and is of considerable interest because of its potential applications to areas such as automatic toll collection, traffic law enforcement and security control of restricted areas. This paper proposes an automatic license plate recognition system for Persian license plates. We have different type of

A. Broumandnia; M. Fathi

2005-01-01

99

Pattern-Recognition Algorithm for Locking Laser Frequency  

NASA Technical Reports Server (NTRS)

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.

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

2006-01-01

100

The effect of image enhancement on biomedical pattern recognition  

Microsoft Academic Search

Image enhancement has been an area of active research for decades. Most studies were aimed at improving the quality of image display for better visualization. Yet few studies have been conducted to investigate the impact of image enhancement on biomedical pattern recognition. In this paper, we examine quantitatively the effect of image enhancement on the performance of biomedical pattern recognition.

Qiang Wut; Yu-Ping Wangt; Zhongmin Liu; Tiehan Chent; K. R. Castleman

2002-01-01

101

Learning Driving Behavior by Timed Syntactic Pattern Recognition  

Microsoft Academic Search

We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values of onboard sensors into simple events. Instead of the common syntactic pattern recognition

S. E. Verwer; M. M. De Weerdt; C. Witteveen

2011-01-01

102

The Log-polar Image Representation in Pattern Recognition Tasks  

Microsoft Academic Search

This paper is a review of works about the use of the log- polar image model for pattern recognition purposes. Particular attention is paid to the rotation-and scale- invariant pattern recognition problem, which is simplified by the log-polar mapping. In spite of this advantage, ordinary translations become a complicated image transform in the log- polar domain. Two approaches addressing the

V. Javier Traver; Filiberto Pla

2003-01-01

103

Statistical pattern recognition techniques as applied to radar returns  

Microsoft Academic Search

This report presents a summary of the basic principles of pattern recognition and statistical decision theory and applies them to the problem of classifying radar returns. While pattern recognition techniques have been applied to radar signal detection problems, they have rarely been used in testing hypothesis for classifying radar returns. Two techniques, the parametric Bayes and the non-parametric K-Nearest Neighbor

W. A. Fordon; A. A. Fraser

1981-01-01

104

Robust and efficient multiclass SVM models for phrase pattern recognition  

Microsoft Academic Search

Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, and noun phrase (NP)-chunking. Even though they yield very accurate results, they are not efficient for online applications, which need

Yu-chieh Wu; Yue-shi Lee; Jie-chi Yang

2008-01-01

105

Pattern recognition: A basis for remote sensing data analysis  

NASA Technical Reports Server (NTRS)

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.

Swain, P. H.

1973-01-01

106

EMG pattern recognition based on artificial intelligence techniques  

Microsoft Academic Search

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition

Sang-Hui Park; Seok-Pil Lee

1998-01-01

107

Image pattern recognition supporting interactive analysis and graphical visualization  

NASA Technical Reports Server (NTRS)

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.

Coggins, James M.

1992-01-01

108

Pattern Recognition with a RICH detector  

NASA Astrophysics Data System (ADS)

The Relativistic Heavy Ion Collider (RHIC) at the Brookhaven National Lab will allow the study of Au-Au collisions at sqrt(s) = 200 GeV/A. It has been proposed that the Solenoidal Tracker at RHIC (STAR) include a Ring Imaging Cherenkov (RICH) detector to augment existing particle identification capabilities. It will allow particle identification for pions and kaons up to a momentum of 3 GeV/c and for protons up to 5 GeV/c. The RICH detector module was developed as a prototype for the ALICE experiment at the CERN/LHC. It employs a 1 cm thick liquid C_6F_14 radiator for the Cherenkov photon production which is contained in a quartz vessel followed by a 10 cm proximity focusing gap. The photons are converted into photo-electrons via a thin (500 nm) CsI layer on a cathode pad-plane of a Multi Wire Proportional Chamber. The amplitude information from the pads allows the reconstruction of the electron position. Simulations of the RICH detector response in the STAR detector environment, the pattern recognition algorithm and the particle identification capabilities will be discussed.

Horsley, Matt A.

1998-10-01

109

Joint transform correlator based on CIELAB model with encoding technique for color pattern recognition  

NASA Astrophysics Data System (ADS)

The CIELAB standard color vision model instead of the traditional RGB color model is utilized for polychromatic pattern recognition. The image encoding technique is introduced. The joint transform correlator is set to be the optical configuration. To achieve the distortion invariance in discrimination processes, we have used the minimum average correlation energy approach to yield sharp correlation peak. From the numerical results, it is found that the recognition ability based on CIELAB color specification system is accepted.

Lin, Tiengsheng; Chen, Chulung; Liu, Chengyu; Chen, Yuming

2010-05-01

110

EMG pattern recognition based on artificial intelligence techniques.  

PubMed

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition. PMID:9865887

Park, S H; Lee, S P

1998-12-01

111

A Novel MicroPhotonic Structure for Optical Header Recognition  

Microsoft Academic Search

In this paper, we propose and demonstrate a new MicroPhotonic structure for optical packet header recognition based on the\\u000a integration of an optical cavity, optical components and a photoreceiver array. The structure is inherently immune to optical\\u000a interference thereby routing an optical header within optical cavities to different photo receiver elements to generate the\\u000a autocorrelation function, and hence the recognition

Muhsen Aljada; Kamal Alameh; Adam Osseiran; Khalid Al-begain

2005-01-01

112

Pattern recognition issues on anisotropic smoothed particle hydrodynamics  

NASA Astrophysics Data System (ADS)

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.

Pereira Marinho, Eraldo

2014-03-01

113

Classification and machine recognition of severe weather patterns  

NASA Technical Reports Server (NTRS)

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.

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

1976-01-01

114

An Overview of Statistical Pattern Recognition Techniques for Speaker Verification  

Microsoft Academic Search

Even though the subject of speaker verification has been investigated for several decades, numerous challenges and new opportunities in robust recognition techniques are still being explored. In this overview paper we first provide a brief introduction to statistical pattern recognition techniques that are commonly used for speaker verification. The second part of the paper presents traditional and modern techniques which

Amin Fazel; Shantanu Chakrabartty

2011-01-01

115

Experiments with the n-tuple Method of Pattern Recognition  

Microsoft Academic Search

The n-tuple method of pattern recognition has been simulated on a somewhat larger and more comprehensive scale than previously reported. The nonweighted version has been found to work better than the maximum likelihood weighted version, and to achieve about 93 percent successful recognition of unconstrained hand-printed numerals, but at the cost of about 42 million bits of storage.

J. R. Ullmann

1969-01-01

116

The control of a prosthetic arm by EMG pattern recognition  

Microsoft Academic Search

An electromyographic (EMG) signal pattern recognition system is constructed for real-time control of a prosthetic arm through precise identification of motion and speed command. A probabilistic model of the EMG patterns is first formulated in the feature space of integral absolute value (IAV). Then, the sample probability density function of pattern classes in the feature space of variance and zero

Sukhan Lee; George N. Saridis

1982-01-01

117

Recognition of affect based on gait patterns.  

PubMed

To provide a means for recognition of affect from a distance, this paper analyzes the capability of gait to reveal a person's affective state. We address interindividual versus person-dependent recognition, recognition based on discrete affective states versus recognition based on affective dimensions, and efficient feature extraction with respect to affect. Principal component analysis (PCA), kernel PCA, linear discriminant analysis, and general discriminant analysis are compared to either reduce temporal information in gait or extract relevant features for classification. Although expression of affect in gait is covered by the primary task of locomotion, person-dependent recognition of motion capture data reaches 95% accuracy based on the observation of a single stride. In particular, different levels of arousal and dominance are suitable for being recognized in gait. It is concluded that gait can be used as an additional modality for the recognition of affect. Application scenarios include monitoring in high-security areas, human-robot interaction, and cognitive home environments. PMID:20350859

Karg, Michelle; Kühnlenz, Kolja; Buss, Martin

2010-08-01

118

Vectorcardiographic Diagnosis Using the Polynomial Discriminant Method of Pattern Recognition.  

National Technical Information Service (NTIS)

The paper describes research on the application of a newly developed pattern-recognition technique called the polynomial discriminant method (PDM) (1) to the diagnosis of heart disease as evidenced in the vectorcardiogram (an orthogonalized form of electr...

D. F. Specht

1966-01-01

119

Visual cluster analysis and pattern recognition template and methods  

DOEpatents

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.

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

1999-05-04

120

Visual cluster analysis and pattern recognition template and methods  

DOEpatents

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.

Osbourn, Gordon Cecil (Albuquerque, NM); Martinez, Rubel Francisco (Albuquerque, NM)

1999-01-01

121

Pattern Recognition of Eeg to Determine Level of Alertness.  

National Technical Information Service (NTIS)

This report documents the work accomplished in applying the principles of pattern recognition technology to the analysis of EEG. Techniques were defined and developed for detection of rapid eye movements and identification of REM epochs. The sleep scoring...

W. B. Martin

1970-01-01

122

Semiconductor optical amplifier switch matrices for optical header recognition  

NASA Astrophysics Data System (ADS)

All-optical header recognition using a tree-structure is reported for a three-bit address. Each bit of a three bit header is read using an optical Sagnac AND gate and the outcome is used to control each level of the three level tree-structure switch. Traffic at 10 Gb/s (payload) is directed through the switch and each possible address outcome is validated. Reflective semiconductor optical amplifiers (RSOAs) are used as the 1 x 2 space switch at each node of the tree-structure switch. A noise propagation analysis that considers mostly amplified spontaneous emission noise is presented. This analysis takes into account the saturation power of the SOAs, their noise figure, the gain of each SOA and the coupled optical power. It is concluded that large switches based on semiconductor optical amplifiers can be constructed using cascaded SOAs.

Dagenais, M.; Ryu, Geunmin; Saini, Simarjeet; Toudeh-Fallah, F.; Gyurek, R.; Donner, P.

2008-03-01

123

Stability as performance metric for subjective pattern recognition - application of Electoral College in face recognition  

Microsoft Academic Search

For a class of pattern recognition problems, such as the face recognition problem, humans do not know the strategies that our brains employ in daily life and therefore there is no algorithm that can emulate our brain ability. Without understanding the psychological processes of brains, an objective of improving accuracy of such systems leads nowhere but to a trial-and-error process

Liang Chen

2008-01-01

124

Adaptive optics to enhance target recognition  

NASA Astrophysics Data System (ADS)

Target recognition can be enhanced by reducing image degradation due to atmospheric turbulence. This is accomplished by an adaptive optic system. We discuss the forms of degradation when a target is viewed through the atmosphere1: scintillation from ground targets on a hot day in visible or infrared light; beam spreading and wavering around in time; atmospheric turbulence caused by motion of the target or by weather. In the case of targets we can use a beacon laser that reflects back from the target into a wavefront detector to measure the effects of turbulence on propagation to and from the target before imaging.1 A deformable mirror then corrects the wavefront shape of the transmitted, reflected or scattered data for enhanced imaging. Further, recognition of targets is enhanced by performing accurate distance measurements to localized parts of the target using lidar. Distance is obtained by sending a short pulse to the target and measuring the time for the pulse to return. There is inadequate time to scan the complete field of view so that the beam must be steered to regions of interest such as extremities of the image during image recognition. Distance is particularly valuable to recognize fine features in range along the target or when segmentation is required to separate a target from background or from other targets. We discuss the issues involved.

McAulay, Alastair D.

2012-05-01

125

Pattern recognition using versatile hybrid joint transform correlators: some techniques for improving the performance  

Microsoft Academic Search

This paper reports the outcome of research studies carried out by us in the area of Optical Pattern Recognition. A hybrid JTC architecture has been used to evaluate correlation performance of four different types of JTCs in a non-cooperative situation. A non-zero order JTC has been proposed based on the principle of differential processing of joint power spectrum and its

G. S. Pati; Kehar Singh

1999-01-01

126

Albedo Pattern Recognition and Time-Series Analyses in Malaysia  

NASA Astrophysics Data System (ADS)

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 trend relationship (R2 = 0.8017), while in 2005 the R2 is 0.4428 of positive linear trend relationship and in 2009 its negative relationship has remarkably change when the R2 is 0.9663 according to the second order polynomial trend line.

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

2012-07-01

127

DNA sequence pattern recognition methods in GRAIL  

SciTech Connect

The goal of the GRAIL project has been to create a comprehensive analysis environment where a host of questions about genes and genome structure can be answered as quickly and accurately as possible. Constructing this system has entailed solving a number of significant technical challenges including: (a) making coding recognition in sequence more sensitive and accurate, (b) compensating for isochore base compositional effects in coding prediction, (c) developing methods to determine which parts of each strand of a long genomic DNA are the coding strand, (d) improving the accuracy of splice site prediction and recognizing non-consensus sites, and (e) recognizing variable regulatory structures such as polymerase II promoters. An additional challenge has been to construct algorithms which compensate for the deleterious effects of insertion or deletion (indel) errors in the coding region recognition process. This paper addresses progress on these technical issues and the current state of sequence feature recognition methods.

Uberbacher, E.C.; Xu, Ying; Shah, M.; Matis, S.; Guan, X.; Mural, R.J.

1995-12-31

128

Reseaux de Neurones Artificiels: Application a la Reconnaissance Optique de Partitions Musicales (Artificial Neural Networks: Application to the Optical Recognition of Music Partitions).  

National Technical Information Service (NTIS)

The development and use of artificial neural networks to solve an unusual pattern recognition problem, those of optical musical score recognition, are discussed. A bibliographical study of the main connectionist models, the most frequently used in the pat...

P. Martin

1992-01-01

129

Sensitive detection of explosives via fluorescence quenching and pattern recognition technique  

NASA Astrophysics Data System (ADS)

There is significant interest in developing chemical sensors for detection of trace explosives. Optical sensors are inherently very sensitive and have potential to detect explosive vapors at room temperature and ambient conditions. There is a need to develop materials for optical sensors to fabricate a sensor array which can provide required sensitivity and selectivity. Here, we report an optical sensor array combined with pattern recognition technique for sensitive and selective detection of explosives. The optical sensor array consists of four conjugated polymers. These polymers have good quantum yield of fluorescence and large Stoke's shift. We have shown that by employing pattern recognition technique, the presence of nitro containing explosive TNT (2,4,6-trinitro toluene) can be discriminated with other common chemical intereferants in 60 seconds.

Kumar, Abhishek; Anandakathir, Robinson; Cho, Jung Hwan; Kurup, Pradeep; Kumar, Jayant

2011-04-01

130

Complements to 'Pattern Recognition and Neural Networks  

Microsoft Academic Search

Introduction Page 4: The book by Przytula & Prasanna (1993) discusses in detail the parallel implementation of neural networks. Page 16: Langley (1996) provides a book-length introduction to one viewpointon machine learning. Langley & Simon (1995) and Bratko & Muggleton (1995) discuss applications of machine learning with claimed real-world benefits. Valentin et al. (1994) survey recent developments in face recognition.

B. d. Ripley

1996-01-01

131

Postprocessing for character recognition using pattern features and linguistic information  

NASA Astrophysics Data System (ADS)

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).

Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

1993-04-01

132

Proposal of anticipatory pattern recognition for EMG prosthetic hand control  

Microsoft Academic Search

In the paper, we proposed an anticipatory pattern recognition method for an electromyogram (EMG) prosthetic hand control. To detect humans' motor intentions as fast as possible, we focused on the transient state of the EMG signals. We sampled the EMG signals from a human forearm, and extracted a feature vector every 50 ms. At each time window, the proposed pattern

Toshiyuki Kondo; Osamu Amagi; Takayuki Nozawa

2008-01-01

133

Driving Pattern Recognition for Control of Hybrid Electric Trucks  

Microsoft Academic Search

The design procedure for an adaptive power management control strategy, based on a driving pattern recognition algorithm is proposed. The design goal of the control strategy is to minimize fuel consumption and engine-out NOx and PM emissions on a set of diversified driving schedules. Six representative driving patterns (RDP) are designed to represent different driving scenarios. For each RDP, the

Chan-Chiao Lin; Soonil Jeon; Huei Peng; Jang Moo Lee

2004-01-01

134

A Pattern-Recognition Approach for Driving Skill Characterization  

Microsoft Academic Search

Information about a driver's driving skill can be used to adapt vehicle control parameters to facilitate the specific driver's needs in terms of vehicle performance and safety. This paper presents an approach to driving skill characterization from a pattern-recognition perspective. The basic idea is to extract patterns that reflect the driver's driving skill level from the measurements of the driver's

Yilu Zhang; William C. Lin; Yuen-Kwok Steve Chin

2010-01-01

135

Pattern matching approach towards real-time traffic sign recognition  

Microsoft Academic Search

This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the unknown sign's shape with standard shapes of the traffic signs. The pattern matching algorithm works with shape vertices rather than the whole image. This reduces the

Hasan Fleyeh; Taha Khan

2010-01-01

136

Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?  

Microsoft Academic Search

This paper discusses the ability of multilayer perceptrons (MLPs) to model the probability distribution of data in typical pattern recognition and verification problems. It is proven that multilayer perceptrons with sigmoidal units and a number of hidden units less or equal than the number of inputs are unable to model patterns distributed in typical clusters, since these networks draw open

Marco Gori; Franco Scarselli

1998-01-01

137

Pattern formation in optical resonators  

NASA Astrophysics Data System (ADS)

We review pattern formation in optical resonators. The emphasis is on 'particle-like' structures such as vortices or spatial solitons. On the one hand, similarities impose themselves with other fields of physics (condensed matter, phase transitions, particle physics, fluds/super fluids). On the other hand the feedback is led by the resonator mirrors to bi- and multi-stability of the spatial field structure, which is the basic ingredient for optical information processing. The spatial dimension or the 'parallelism' is the strength of optics compared to electronics (and will have to be employed to fully use the advantages optics offers in information processing). But even in the 'serial' processing tasks of telecoms (e.g. information buffering) spatial resonator solitons can do better than the schemes proposed so far—including 'slow light'. Pattern formation in optical resonators will likely be the key to brain-like information processing like cognition, learning and association; to complement the precise but limited algorithmic capabilities of electronic processing. But even in the short term it will be useful for solving serial optical processing problems. The prospects for technical uses of pattern formation in resonators are one motivation for this research. The fundamental similarities with other fields of physics, on the other hand, inspire transfer of concepts between fields; something that has always proven fruitful for gaining deeper insights or for solving technical problems.

Weiss, C. O.; Larionova, Ye

2007-02-01

138

Optical music recognition system which learns  

NASA Astrophysics Data System (ADS)

This paper describes an optical music recognition system composed of a database and three interdependent processes: a recognizer, an editor, and a learner. Given a scanned image of a musical score, the recognizer locates, separates, and classifies symbols into musically meaningful categories. This classification is based on the k-nearest neighbor method using a subset of the database that contains features of symbols classified in previous recognition sessions. Output of the recognizer is corrected by a musically trained human operator using a music notation editor. The editor provides both visual and high-quality audio feedback of the output. Editorial corrections made by the operator are passed to the learner which then adds the newly acquired data to the database. The learner's main task, however, involves selecting a subset of the database and reweighing the importance of the features to improve accuracy and speed for subsequent sessions. Good preliminary results have been obtained with everything from professionally engraved scores to hand-written manuscripts.

Fujinaga, Ichiro

1993-01-01

139

Partial discharge pattern recognition of current transformers using an ENN  

Microsoft Academic Search

This paper proposes an extension-neural-network (ENN)-based recognition method to identify the partial-discharge (PD) patterns of high-voltage current transformers (HVCTs). First, a commercial PD detector is used to measure the three-dimensional (3D) PD patterns of cast-resin HVCTs, then three data preprocessing schemes that extract relevant features from the raw 3-D PD patterns are presented for the proposed ENN-based classifier. The ENN

Mang-Hui Wang

2005-01-01

140

Quantum pattern recognition with liquid-state nuclear magnetic resonance  

NASA Astrophysics Data System (ADS)

A quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian. In contrast to classic neural networks, the algorithm can return a quantum superposition of multiple recognized patterns. A proof of principle for the algorithm for two qubits is provided using a liquid-state NMR quantum computer.

Neigovzen, Rodion; Neves, Jorge L.; Sollacher, Rudolf; Glaser, Steffen J.

2009-04-01

141

Pattern Recognition Approach to Neuropathy and Neuronopathy  

PubMed Central

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.

Barohn, Richard J; Amato, Anthony A.

2014-01-01

142

Detection and recognition of analytes based on their crystallization patterns  

DOEpatents

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.

Morozov, Victor (Manassas, VA) [Manassas, VA; Bailey, Charles L. (Cross Junction, VA) [Cross Junction, VA; Vsevolodov, Nikolai N. (Kensington, MD) [Kensington, MD; Elliott, Adam (Manassas, VA) [Manassas, VA

2008-05-06

143

Deterministic walks as an algorithm of pattern recognition  

NASA Astrophysics Data System (ADS)

New tools for automatically finding data clusters that share statistical properties in a heterogeneous data set are imperative in pattern recognition research. Here we introduce a deterministic procedure as a tool for pattern recognition in a hierarchical way. The algorithm finds attractors of mutually close points based on the neighborhood ranking. A memory parameter ? acts as a hierarchy parameter, in which the clusters are identified. The final result of the method is a general tree that represents the nesting structure of the data in an invariant way by scale transformation.

Campiteli, Mônica G.; Batista, Pablo D.; Kinouchi, Osame; Martinez, Alexandre S.

2006-08-01

144

Pattern recognition receptors in innate immunity, host defense, and immunopathology.  

PubMed

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. An improved understanding of the pattern recognition receptors that mediate innate responses and their downstream effects after receptor ligation has the potential to lead to new ways to improve vaccines and prevent autoimmunity. This review focuses on the control of innate immune activation and the role that innate immune receptors play in helping to maintain tissue homeostasis. PMID:24292903

Suresh, Rahul; Mosser, David M

2013-12-01

145

Comparative effects of optical-correlator signal-dependent and signal-independent noise on pattern-recognition performance with the phase-only filter  

NASA Astrophysics Data System (ADS)

The comparative effects of optical-correlator signal-dependent and additive signal-independent noise on correlation-filter performance are analyzed by three different performance measures. For an identical value of the signal-to-noise ratio imposed on each type of noise in a binary input image, computer simulations performed with the phase-only filter show (i) that additive Gaussian signal-independent noise yields a much larger correlation-performance degradation than signal-dependent noise and (ii) that the different types of signal-dependent noise lead to similar correlation results because of similar effects on the input image that are inherent to the nature of the noise.

Terrillon, Jean-Christophe

1995-11-01

146

Pattern recognition on a quantum computer  

SciTech Connect

By means of a simple example, it is demonstrated that the task of finding and identifying certain patterns in an otherwise (macroscopically) unstructured picture (dataset) can be accomplished efficiently by a quantum computer. Employing the powerful tool of the quantum Fourier transform, the proposed quantum algorithm exhibits an exponential speedup in comparison with its classical counterpart.

Schuetzhold, Ralf [Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, V6T 1Z1 (Canada)

2003-06-01

147

Pattern-recognition software for plant surveillance  

Microsoft Academic Search

A power plant in steady-state operation has almost all plant signals in definite relations to each other, and a new approach to plant surveillance, which compares the patterns representing these relations, has been used to measure the consistency of many plant signals simultaneously and so derive a measure of the validity of each individual signal. In the application of this

J. Mott; J. Young; W. Bertch; R. W. King

1987-01-01

148

Face recognition based on logarithmic local binary patterns  

NASA Astrophysics Data System (ADS)

This paper presents a novel approach to the problem of face recognition that combines the classical Local Binary Pattern (LBP) feature descriptors with image processing in the logarithmic domain and the human visual system. Particularly, we have introduced parameterized logarithmic image processing (PLIP) operators based LBP feature extractor. We also use the human visual system based image decomposition, which is based on the Weber's law to extract features from the decomposed images and combine those with the features extracted from the original images thereby enriching the feature vector set and obtaining improved rates of recognition. Comparisons with other methods are also presented. Extensive experiments clearly show the superiority of the proposed scheme over LBP feature descriptors. Recognition rates as high as 99% can be achieved as compared to the recognition rate of 96.5% achieved by the classical LBP using the AT&T Laboratories face database.

Mandal, Debashree; Panetta, Karen; Agaian, Sos

2013-02-01

149

Control of antiviral immunity by pattern recognition and the microbiome  

PubMed Central

Summary Human skin and mucosal surfaces are in constant contact with resident and invasive microbes. Recognition of microbial products by receptors of the innate immune system triggers rapid innate defense and transduces signals necessary for initiating and maintaining the adaptive immune responses. Microbial sensing by innate pattern recognition receptors is not restricted to pathogens. Rather, proper development, function, and maintenance of innate and adaptive immunity rely on continuous recognition of products derived from the microorganisms indigenous to the internal and external surfaces of mammalian host. Tonic immune activation by the resident microbiota governs host susceptibility to intestinal and extra-intestinal infections including those caused by viruses. This review highlights recent developments in innate viral recognition leading to adaptive immunity, and discusses potential link between viruses, microbiota and the host immune system. Further, we discuss the possible roles of microbiome in chronic viral infection and pathogenesis of autoimmune disease, and speculate on the benefit for probiotic therapies against such diseases.

Pang, Iris K.; Iwasaki, Akiko

2013-01-01

150

An improved MSD-based method for PD pattern recognition  

Microsoft Academic Search

In this paper a new method for multi source partial discharge (PD) pattern recognition by discrete wavelet transform, is presented. The proposed method is based on the application of multi-resolution signal decomposition (MSD) technique applied to a 3D PD pattern image. The multi-resolution technique has shown that some detail images (horizontal, vertical and diagonal) at different decomposition levels have frequencial

R. Candela; P. Romano

2007-01-01

151

Neural substrates for visual pattern recognition learning in Igo  

Microsoft Academic Search

Different contexts require different visual pattern recognitions even for identical retinal inputs, and acquiring expertise in various visual-cognitive skills requires long-term training to become capable of recognizing relevant visual patterns in otherwise ambiguous stimuli. This 3-Tesla fMRI experiment exploited shikatsu-mondai (life-or-death problems) in the Oriental board game of Igo (Go) to identify the neural substrates supporting this gradual and adaptive

Kosuke Itoh; Hideaki Kitamura; Yukihiko Fujii; Tsutomu Nakada

2008-01-01

152

Learning pattern recognition through quasi-synchronization of phase oscillators.  

PubMed

The idea that synchronized oscillations are important in cognitive tasks is receiving significant attention. In this view, single neurons are no longer elementary computational units. Rather, coherent oscillating groups of neurons are seen as nodes of networks performing cognitive tasks. From this assumption, we develop a model of stimulus-pattern learning and recognition. The three most salient features of our model are: 1) a new definition of synchronization; 2) demonstrated robustness in the presence of noise; and 3) pattern learning. PMID:21075723

Vassilieva, Ekaterina; Pinto, Guillaume; de Barros, José Acacio; Suppes, Patrick

2011-01-01

153

Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition  

NASA Technical Reports Server (NTRS)

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.

Amador, Jose J (Inventor)

2007-01-01

154

Human-Computer Interaction for Complex Pattern Recognition Problems  

Microsoft Academic Search

We review some applications of human-computer interaction that alleviate the complexity of visual recognition by partitioning it into human and machine tasks to exploit the differences between human and machine capabilities. Human involvement offers advantages, both in the design of automated pattern classification systems, and at the operational level of some image retrieval and classification tasks. Recent development of interactive

Jie Zou; George Nagy

155

Identifying travel mode from GPS trajectories through fuzzy pattern recognition  

Microsoft Academic Search

Although GPS-based travel survey has been studied by many, automated travel mode detection still remains a technical challenge. This paper proposed and tested a fuzzy approach to travel mode recognition from the GPS travel data collected from 32 volunteers for 142 days in Shanghai. Four speed-related fuzzy variables were selected to characterize five movement patterns (walk, bike, bus, rail, and

Chao Xu; Minhe Ji; Wen Chen; Zhihua Zhang

2010-01-01

156

Fuzzy Pattern Recognition Approach to Construction Contractor Selection  

Microsoft Academic Search

Contractor selection is a complex process crucial to ensuring the success of construction projects. Existing methods by which owners select a suitable contractor have been inadequate because it is difficult for decision-makers to evaluate contractor bids against inexact qualitative criteria. The purpose of this paper is to propose a Multiple-layer Fuzzy Pattern Recognition (MFPR) approach to solve contractor selection problem.

Li Yawei; Chen Shouyu; Nie Xiangtian

2005-01-01

157

Distortion invariant pattern recognition using Fourier plane nonlinear filters  

Microsoft Academic Search

The use of nonlinear techniques in the Fourier plane of pattern recognition correlators can improve the correlators' performance in terms of discrimination against objects similar to the target object, correlation peak sharpness, and correlation noise robustness. Additionally, filter designs have been proposed which provide the linear correlator with invariance properties with respect to input signal distortions and rotations. In this

Bahram Javidi; Dean Painchaud

1996-01-01

158

Pattern Recognition as a Guide in Diagnosing Headache  

PubMed Central

This paper presents the family physician with a comprehensive yet simple classification that is easily followed. By a system known as ‘pattern recognition’, the physician may determine cause in the majority of headaches in his own office. With use of ancillary investigatory procedures where necessary, he will refer only when the diagnosis warrants it.

Nurse, E. G.

1974-01-01

159

Improved SPC chart pattern recognition using statistical features  

Microsoft Academic Search

Increasingly rapid changes and highly precise manufacturing environments require timely monitoring and intervention when deemed necessary. Traditional Statistical Process Control (SPC) charting, a popular monitoring and diagnosis tool, is being improved to be more sensitive to small changes and to include more intelligence to handle dynamic process information. Artificial neural network-based SPC chart pattern recognition schemes have been introduced by

A. Hassan; M. Shariff Nabi Baksh; A. M. Shaharoun; H. Jamaluddin

2003-01-01

160

Statistical pattern recognition with neural networks: benchmarking studies  

Microsoft Academic Search

Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization

Teuvo Kohonen; Gyorgy Barna; Ronald Chrisley

1988-01-01

161

EMG pattern recognition based on evidence accumulation for prosthesis control  

Microsoft Academic Search

The authors present an EMG pattern recognition method to identify motion command for the control of a prosthetic arm by evidence accumulation with multiple parameters. The adaptive cepstrum coefficients which the authors propose in this paper, integral absolute value (IAV), difference absolute mean value (DAMV), variance and autoregressive (AR) model coefficients, are extracted as parameters by probabilistic and stochastic models.

Seok-Pil Lee; Sang-Hui Park; Jeong-Seop Kim; Ig-Jae Kim

1996-01-01

162

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

PubMed

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. PMID:12567945

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

1998-04-01

163

Recall, recognition, and confidence patterns in eyewitness testimony  

Microsoft Academic Search

SUMMARY The diversity of methods, contents and tests used in the study of eyewitness memory may have contributed to discrepancies in results in this field. In this experiment, using incidental or intentional learning, we examine the recall and recognition of actions and details concerning the central and peripheral information of a kidnapping. A similar pattern emerges in free recall, hits

Malen Migueles; Elvira Garcia-Bajos

1999-01-01

164

Pattern recognition for identification of lysozyme droplet solution chemistry.  

PubMed

Pattern formation during evaporation of a colloidal sessile droplet is a phenomenon relevant to a wide variety of scientific disciplines. The patterns remaining on the substrate are indicative of the transport mechanisms and phase transitions occurring during evaporation and may reflect the solution chemistry of the fluid [1-18]. Pattern formation during evaporation of droplets of biofluids has also been examined and these complex patterns may reflect the health of the patient [23-31]. Automatic detection of variations in the fluid composition based on these deposit patterns could lead to rapid screening for diagnostic or quality control purposes. In this study, a pattern recognition algorithm is presented to differentiate between deposits containing various solution compositions. The deposits studied are from droplets of simplified, model biological fluids of aqueous lysozyme and NaCl solutions. For the solution concentrations examined here, the deposit patterns are dependent upon the initial solution composition. Deposit images are represented by extracting features using the Gabor wavelet, similar to the method used for iris recognition. Two popular pattern recognition algorithms are used to classify the deposits. The k-means clustering algorithm is used to test if incremental changes in solution concentration result in reproducible and statistically interpretable variations in the deposit patterns. The k-nearest neighbor algorithm is also used to classify the deposit images by solution concentration based on a set of training images for each class. Here, we demonstrate that the deposit patterns may act as a "fingerprint" for identification of solution chemistry. The results of this study are very promising, with classification accuracies of 90-97.5%. PMID:24342799

Gorr, Heather Meloy; Xiong, Ziye; Barnard, John A

2014-03-01

165

ANN-TREE: a hybrid method for pattern recognition  

NASA Astrophysics Data System (ADS)

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.

Zhou, Lijia; Franklin, Stan

1993-09-01

166

A new concept of vertically integrated pattern recognition associative memory  

SciTech Connect

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.

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

2011-11-01

167

[Analysis of liquor flavor spectra and pattern recognition computation].  

PubMed

Chinese liquor is a complex mixture and contains a large amount of microconstituents, which affects the quality and flavor of liquor. In order to discriminate liquor flavors rapidly, the spectra of liquors were obtained by FTIR and employed as the input patterns of pattern classification algorithms, then liquor flavor discrimination models were built. This paper introduces liquor flavor pattern recognition algorithms comprehensively and systematically for the first time, and the algorithms contain statistical classifications (linear discriminant function, quadratic discriminant function, regularized discriminant analysis, and K nearest neighbor), prototype learning algorithm (learning vector quantization), support vector machine and adaboost algorithm. Experimental results show that the liquor flavor classification algorithms demonstrate good performance and achieve high accuracy, recognition rate and rejection rate. PMID:20545131

Jiang, An; Peng, Jiang-Tao; Peng, Si-Long; Wei, Ji-Ping; Li, Chang-Wen

2010-04-01

168

A Voting-Based Sequential Pattern Recognition Method  

PubMed Central

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method.

Ogawara, Koichi; Fukutomi, Masahiro; Uchida, Seiichi; Feng, Yaokai

2013-01-01

169

Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms  

NASA Astrophysics Data System (ADS)

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.

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

2013-02-01

170

Object detection by optical correlator and intelligence recognition surveillance systems  

NASA Astrophysics Data System (ADS)

We report a recent work on robust object detection in high-resolution aerial imagery in urban environment for Intelligence, Surveillance and Recognition (ISR) missions. Our approaches used the simple linear iterative clustering (SLIC) algorithm, which combines regional and edge information to form the superpixels. The irregularity in size and shape of the superpixels measured with the Hausdorff distance served to determine the salient regions in the very large aerial images. Then, the car detection was performed with both the component-based approach and the featurebased approaches. We merged the superpixels with the statistical region merging (SRM) algorithm. The regions were described by the radiometric, geometrical moments and shape features, and classified using the Support Vector Machine (SVM). The cast shadow were detected and removed by a radiometry based tricolor attenuation model (TAM). Detection of object parts is less sensitive to occlusion, rotation, and changes in scale, view angle and illumination than detection of the object as whole. The object parts were combined to the object according to their unique spatial relations. On the other hand, we used the invariant scale invariant feature transform (SIFT) features to describe superpixels and classed them by the SVM as belong or not to the object. All along our recent work we still trace the brilliant ideas in early days by H. John Caulfield and other pioneers of optical pattern recognition, for improving the discrimination of the matched spatial filter with linear combinations of cross-correlations, which have been inherited transformed and reinvented to achieve tremendous progress.

Sheng, Yunlong

2013-09-01

171

Pattern Recognition Software and Techniques for Biological Image Analysis  

PubMed Central

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.

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

2010-01-01

172

Centromere and cytoplasmic staining pattern recognition: a local approach.  

PubMed

Autoimmune diseases are very serious and also invalidating illnesses. The benchmark procedure for their diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 wells and then report the staining pattern. Despite its pivotal role, IIF is affected by inter- and intra-laboratory variabilities demanding for the development of computer-aided-diagnosis tools supporting medical doctor decisions. With reference to staining pattern recognition, state-of-the-art approaches recognize five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that recent work suggests to be harder than the classification itself. In this paper, we extend the panel of detectable HEp-2 staining patterns, introducing the recognition of centromere and cytoplasmic patterns, which have a high specific match with certain autoimmune diseases, from other stainings. Since image segmentation algorithms fail on these samples, we developed a classification system integrating local descriptors and the bag of visual word approach, which represents image contents without the burden of segmentation. We tested our approach on a large dataset of HEp-2 images with high variability in both fluorescence intensity and staining patterns correctly recognizing the 97.12 % of samples. The system has also been validated in a daily routine fashion on 108 consecutive IIF analyses of hospital outpatients and inpatients, achieving an accuracy rate of 97.22 %. PMID:23877232

Iannello, Giulio; Onofri, Leonardo; Soda, Paolo

2013-12-01

173

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays  

PubMed Central

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.

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

2012-01-01

174

A pattern recognition system for JPEG steganography detection  

NASA Astrophysics Data System (ADS)

This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

Chen, C. L. Philip; Chen, Mei-Ching; Agaian, Sos; Zhou, Yicong; Roy, Anuradha; Rodriguez, Benjamin M.

2012-10-01

175

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

NASA Technical Reports Server (NTRS)

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.

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

1975-01-01

176

Some results on contractive mappings as related to pattern recognition  

NASA Technical Reports Server (NTRS)

Several of the techniques used in pattern recognition are reformulated as the problem of determining fixed points of a function. If x sub 0 is a fixed point of f and if f is contractive at x sub 0, then, for any y belonging to a sufficiently small neighborhood of x sub 0 the orbit of y will converge to x sub 0. Several general results regarding contractive mappings are developed with emphasis on functions.

Brown, D. R.; Malley, M. J.

1975-01-01

177

Linear programming and its application to pattern recognition problems  

NASA Technical Reports Server (NTRS)

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.

Omalley, M. J.

1973-01-01

178

A fuzzy pattern recognition method to classify esophageal motility records  

Microsoft Academic Search

Analysis and classification of esophageal motility records were investigated using signal processing and fuzzy-set pattern\\u000a recognition techniques. A set of parameters has been extracted from the raw records and has previously been used as characterizing\\u000a features. Improvements to these procedures were obtained by extracting these features from processed data, and some additional\\u000a parameters were developed. The new set of features

Fatma E. Z. Abou-Chadi; F. A. Ezzat; A. A. Sif El-Din

1994-01-01

179

Pattern recognition used to investigate multivariate data in analytical chemistry  

SciTech Connect

Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

Jurs, P.C.

1986-06-06

180

Pattern recognition of gas sensor array using characteristics of impedance  

Microsoft Academic Search

In this paper, impedance variations of SnO2-based gas sensor array are measured in gases. Back propagation algorithm of pattern recognition [1,2] is used to distinguish a species and concentration of gases with analog signals of impedance variation from sensor array. When the impedance variation presents as the quantities of rising and falling time to 100kHz pulse input signal of a

Byung-Su Joo; Nak-Jin Choi; Yun-Su Lee; Jun-Woo Lim; Bong-Hwi Kang; Duk-Dong Lee

2001-01-01

181

Musical instrument identification: A pattern-recognition approach  

Microsoft Academic Search

A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic features— related to the physical properties of source excitation and resonance structure—were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included

Keith D. Martin; Youngmoo E. Kim

1998-01-01

182

Pattern recognition for Space Applications Center director's discretionary fund  

NASA Technical Reports Server (NTRS)

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,

Singley, M. E.

1984-01-01

183

Cerebellar involvement in metabolic disorders: a pattern-recognition approach  

Microsoft Academic Search

Inborn errors of metabolism can affect the cerebellum during development, maturation and later during life. We have established\\u000a criteria for pattern recognition of cerebellar abnormalities in metabolic disorders. The abnormalities can be divided into\\u000a four major groups: cerebellar hypoplasia (CH), hyperplasia, cerebellar atrophy (CA), cerebellar white matter abnormalities\\u000a (WMA) or swelling, and involvement of the dentate nuclei (DN) or cerebellar

M. Steinlin; S. Blaser; E. Boltshauser

1998-01-01

184

Effects of identical context on visual pattern recognition by pigeons  

Microsoft Academic Search

The effects of identical context on pattern recognition by pigeons for outline drawings of faces were investigated by training\\u000a pigeons to identify (Experiment 1) and categorize (Experiment 2) these stimuli according to the orientation of the mouth—an\\u000a upright U shape representing a smiling mouth or an inverted U shape representing a sad mouth. These target stimuli were presented\\u000a alone (Pair

Francisco J. Donis; Sheila Chase; Eric G. Heinemann

2005-01-01

185

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

PubMed

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. PMID:12567936

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

1998-03-01

186

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements  

NASA Astrophysics Data System (ADS)

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Na, Seung Y.; Park, Min S.; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

187

Recognition of dynamic patterns in dc-dc switching converters  

NASA Astrophysics Data System (ADS)

Techniques are presented for analyzing the dynamic patterns of power flow in high-frequency dc-dc switching converters. Emphasis is placed on exploring methods for developing reduced-order models of power electronic circuits for simulation and state-space averaging. Tools from selective modal analysis are used to develop the dynamic pattern recognition algorithm that facilitates the classification of circuit modules. The algorithm is intended for the analysis of converters derived from the canonical switching call, i.e., the buck, boost, and flyback converters.

Leeb, Steven B.; Kirtley, James L.; Verghese, George C.

188

Innate sensing of viruses by pattern recognition receptors in birds.  

PubMed

Similar to mammals, several viral-sensing pattern recognition receptors (PRR) have been identified in birds including Toll-like receptors (TLR) and retinoic acid-inducible gene I (RIG-I)-like receptors (RLR). Avian TLR are slightly different from their mammalian counterparts, including the pseudogene TLR8, the absence of TLR9, and the presence of TLR1La, TLR1Lb, TLR15, and TLR21. Avian TLR3 and TLR7 are involved in RNA virus recognition, especially highly pathogenic avian influenza virus (HPAIV), while TLR15 and TLR21 are potential sensors that recognize both RNA viruses and bacteria. However, the agonist of TLR15 is still unknown. Interestingly, chickens, unlike ducks, geese and finches, lack RIG-I, however they do express melanoma differentiation-associated gene 5 (MDA5) which functionally compensates for the absence of RIG-I. Duck RIG-I is the cytosolic recognition element for HPAIV recognition, while chicken cells sense HPAIV through MDA5. However, the contributions of MDA5 and RIG-I to IFN-? induction upon HPAIV infection is different, and this may contribute to the chicken's susceptibility to highly pathogenic influenza. It is noteworthy that the interactions between avian DNA viruses and PRR have not yet been reported. Furthermore, the role for avian Nod-like receptors (NLR) in viral immunity is largely unknown. In this review, recent advances in the field of viral recognition by different types of PRR in birds are summarized. In particular, the tissue and cellular distribution of avian PRR, the recognition and activation of PRR by viruses, and the subsequent expression of innate antiviral genes such as type I IFN and proinflammatory cytokines are discussed. PMID:24016341

Chen, Shun; Cheng, Anchun; Wang, Mingshu

2013-01-01

189

Innate sensing of viruses by pattern recognition receptors in birds  

PubMed Central

Similar to mammals, several viral-sensing pattern recognition receptors (PRR) have been identified in birds including Toll-like receptors (TLR) and retinoic acid-inducible gene I (RIG-I)-like receptors (RLR). Avian TLR are slightly different from their mammalian counterparts, including the pseudogene TLR8, the absence of TLR9, and the presence of TLR1La, TLR1Lb, TLR15, and TLR21. Avian TLR3 and TLR7 are involved in RNA virus recognition, especially highly pathogenic avian influenza virus (HPAIV), while TLR15 and TLR21 are potential sensors that recognize both RNA viruses and bacteria. However, the agonist of TLR15 is still unknown. Interestingly, chickens, unlike ducks, geese and finches, lack RIG-I, however they do express melanoma differentiation-associated gene 5 (MDA5) which functionally compensates for the absence of RIG-I. Duck RIG-I is the cytosolic recognition element for HPAIV recognition, while chicken cells sense HPAIV through MDA5. However, the contributions of MDA5 and RIG-I to IFN-? induction upon HPAIV infection is different, and this may contribute to the chicken’s susceptibility to highly pathogenic influenza. It is noteworthy that the interactions between avian DNA viruses and PRR have not yet been reported. Furthermore, the role for avian Nod-like receptors (NLR) in viral immunity is largely unknown. In this review, recent advances in the field of viral recognition by different types of PRR in birds are summarized. In particular, the tissue and cellular distribution of avian PRR, the recognition and activation of PRR by viruses, and the subsequent expression of innate antiviral genes such as type I IFN and proinflammatory cytokines are discussed.

2013-01-01

190

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition  

PubMed Central

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity.

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

191

Mixed pattern matching-based traffic abnormal behavior recognition.  

PubMed

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Wu, Jian; Cui, Zhiming; Sheng, Victor S; Shi, Yujie; Zhao, Pengpeng

2014-01-01

192

3D CARS image reconstruction and pattern recognition on SHG images  

NASA Astrophysics Data System (ADS)

Nonlinear optical imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or second-harmonic generation (SHG) show great potential for in-vivo investigations of tissue. While the microspectroscopic imaging tools are established, automized data evaluation, i.e. image pattern recognition and automized image classification, of nonlinear optical images still bares great possibilities for future developments towards an objective clinical diagnosis. This contribution details the capability of nonlinear microscopy for both 3D visualization of human tissues and automated discrimination between healthy and diseased patterns using ex-vivo human skin samples. By means of CARS image alignment we show how to obtain a quasi-3D model of a skin biopsy, which allows us to trace the tissue structure in different projections. Furthermore, the potential of automated pattern and organization recognition to distinguish between healthy and keloidal skin tissue is discussed. A first classification algorithm employs the intrinsic geometrical features of collagen, which can be efficiently visualized by SHG microscopy. The shape of the collagen pattern allows conclusions about the physiological state of the skin, as the typical wavy collagen structure of healthy skin is disturbed e.g. in keloid formation. Based on the different collagen patterns a quantitative score characterizing the collagen waviness - and hence reflecting the physiological state of the tissue - is obtained. Further, two additional scoring methods for collagen organization, respectively based on a statistical analysis of the mutual organization of fibers and on FFT, are presented.

Medyukhina, Anna; Vogler, Nadine; Latka, Ines; Dietzek, Benjamin; Cicchi, Riccardo; Pavone, Francesco S.; Popp, Jürgen

2012-05-01

193

Novel video processing schemes integrating image compression and pattern recognition  

NASA Astrophysics Data System (ADS)

In comparison to present security applications, pattern recognition techniques can be categorized as 'hard' automatic target recognition (ATR) and 'soft' ATR. The first category has been established for years and deals with specific object recognition. On the other hand, the second, less established category operates on very fast object class-level recognition only. The second category usually employs very fast processing and an image database. In this paper, we introduce a novel method to integrate a compression technique based on logic data representation with soft ATR. This new compression method applies Arnold's Differential Mapping Singularities Theory in the context of 3D object projection into the 2D image plane, and takes advantage of the fact that object edges can be interpreted in terms of singularities, which can be described isomorphically by simple polynomials. Therefore, compared to state- of-the-art still image compression, such as JPEG, there is no information los in high contrast, high-dynamic range image areas; as a result, the global peak signal noise ratio can be very high By using linked edges to represent an object, it is possible to use a simple set of parameters for real-time 'soft' ATR. This publication discuses various security applications of this new scheme, which is integrated with ATR compression.

Ternovskiy, Igor V.; Jannson, Tomasz

1997-10-01

194

Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?  

PubMed

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. PMID:23278925

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

2013-01-01

195

Recognition as a challenging label-free optical sensing system  

NASA Astrophysics Data System (ADS)

Optical biosensors are increasingly used in application areas of environmental analysis, healthcare and food safety. The quality of the biosensor's results depends on the interaction layer, the detection principles, and evaluation strategies, not only on the biopolymer layer but also especially on recognition elements. Using label-free optical sensing, non-specific interaction between sample and transducer has to be reduced, and the selectivity of recognition elements has to be improved. For this reason, strategies to avoid non-specific interaction even in blood and milk are discussed, a variety of upcoming recognition is given. Based on the classification of direct optical detection methods, some examples for the above mentioned applications are reviewed. Trends as well as advantages of parallel multisport detection for kinetic evaluation are also part of the lecture.

Gauglitz, Günter

2013-05-01

196

Adaptive-optical radial-basis-function neural network for handwritten digit recognition  

NASA Astrophysics Data System (ADS)

An adaptive optical radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially- multiplexed system incorporating on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input and 198 stored reference patterns in parallel using dual vector-matrix multipliers. For this experimental software is used to perform the on-line learning of the weights and basis function widths. An experimental recognition rate of 86.7% correct out of 300 testing samples is achieved with the adaptive training versus 52.3% correct for non-adaptive training. The experimental results from the optical system are compared with data from a computer model of the system in order to identify noise sources and indicate possible improvements for system performance.

Foor, Wesley E.; Neifeld, Mark A.

1994-06-01

197

Robust non-parametric probabilistic image processing for face recognition and pattern recognition  

NASA Astrophysics Data System (ADS)

Face Recognition has been a pattern recognition application of great interest. Many mathematical models have been used for face recognition and among them probabilistic methods However, up to now probabilistic methods rely heavily on the number of training data and do not fully exploit the 2-dimensional information of the images, both the training and the testing sets. In this paper's method a new 2-D robust probabilistic method of transforming the principal components of the initial image data, allowing support vector machines to efficiently capture the inference between images. This new algorithm encodes every image with the help of Robust Kernel non Parametric Estimation and in the second stage uses Support Vector Machines to classify this encoded information. Results exhibit that Non Parametric Estimation of the Probability Function of the image highlights the unique characteristics of each person making it easier for classifiers to group those instances and efficiently perform the classification of the images and thus leading to better results compared to up to date methods for face recognition.

Pavlidou, Meropi; Zioutas, George

2014-04-01

198

Study on pattern recognition of the quality control chart based on neural network  

Microsoft Academic Search

The quality control chart is the main tool of quality control in a FMS. The pattern recognition of the quality control chart using a neural network is presented. Thus the pattern recognition of the quality control chart can be implemented automatically. The configuration of the neural network is simple, the capability of recognition is more powerful and training time is

Ping Chen; Junqin Liu; Jing Luo

2002-01-01

199

Issues in evolving GP based classifiers for a pattern recognition task  

Microsoft Academic Search

This paper discusses issues when evolving genetic programming (GP) classifiers for a pattern recognition task such as handwritten digit recognition. Developing elegant solutions for handwritten digit classification is a challenging task. Similarly, design and training of classifiers using genetic programming is a relatively new approach in pattern recognition as compared to other traditional techniques. Several strategies for GP training are

Ankur M. Teredesai; Venu Govindaraju

2004-01-01

200

Collocation and Pattern Recognition Effects on System Failure Remediation  

NASA Technical Reports Server (NTRS)

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.

Trujillo, Anna C.; Press, Hayes N.

2007-01-01

201

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation  

NASA Technical Reports Server (NTRS)

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.

Restrepo, Carolina; Hurtado, John E.

2011-01-01

202

Neural substrates for visual pattern recognition learning in Igo.  

PubMed

Different contexts require different visual pattern recognitions even for identical retinal inputs, and acquiring expertise in various visual-cognitive skills requires long-term training to become capable of recognizing relevant visual patterns in otherwise ambiguous stimuli. This 3-Tesla fMRI experiment exploited shikatsu-mondai (life-or-death problems) in the Oriental board game of Igo (Go) to identify the neural substrates supporting this gradual and adaptive learning. In shikatsu-mondai, the player adds stones to the board with the objective of making, or preventing the opponent from making nigan (two eyes), or the topology of figure of eight, with these stones. Without learning the game, passive viewing of shikatsu-mondai activated the occipito-temporal cortices, reflecting visual processing without the recognition of nigan. Several days after two-hour training, passive viewing of the same stimuli additionally activated the premotor area, intraparietal sulcus, and a visual area near the junction of the (left) intraparietal and transverse occipital sulci, demonstrating plastic changes in neuronal responsivity to the stimuli that contained indications of nigan. Behavioral tests confirmed that the participants had successfully learned to recognize nigan and solve the problems. In the newly activated regions, the level of neural activity while viewing the problems correlated positively with the level of achievement in learning. These results conformed to the hypothesis that recognition of a newly learned visual pattern is supported by the activities of fronto-parietal and visual cortical neurons that interact via newly formed functional connections among these regions. These connections would provide the medium by which the fronto-parietal system modulates visual cortical activity to attain behaviorally relevant perceptions. PMID:18621033

Itoh, Kosuke; Kitamura, Hideaki; Fujii, Yukihiko; Nakada, Tsutomu

2008-08-28

203

Predictability of protein subcellular locations by pattern recognition techniques.  

PubMed

An analysis of the predictability of subcellular locations is performed by using simple pattern recognition techniques in an attempt to capture the real dimensions of the problem at hand. Results show that there are some particular locations that does not need of high complexity classification models to be predicted with high accuracies, and some partial biological explanations are formulated. All the experiments were carried out over a set of Arabidopsis Thaliana proteins and classes were defined according to the plants GO slim. PMID:21096466

Jaramillo-Garzon, J A; Perera-Lluna, A; Castellanos-Dominguez, C G

2010-01-01

204

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

NASA Astrophysics Data System (ADS)

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.

Millán, María S.

2012-10-01

205

Survey and bibliography of Arabic optical text recognition  

Microsoft Academic Search

Research work on Arabic optical text recognition (AOTR), although lagging that of other languages, is becoming more intensive than before and commercial systems for AOTR are becoming available. This paper presents a comprehensive survey and bibliography of research on AOTR, by covering all the research publications on AOTR to which the authors had access. This paper introduces the general topic

Badr Al-Badr; Sabri A. Mahmoud

1995-01-01

206

Pattern recognition of satellite cloud imagery for improved weather prediction  

NASA Technical Reports Server (NTRS)

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

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

1986-01-01

207

Resolving the limb position effect in myoelectric pattern recognition.  

PubMed

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. PMID:21846608

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

2011-12-01

208

Adaptive, optical, radial basis function neural network for handwritten digit recognition.  

PubMed

An adaptive, optical, radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially multiplexed system that incorporates an on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel by using dual vector-matrix multipliers and a contrastreversing spatial light modulator. Software is used to emulate an electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7% correct out of 300 testing samples is achieved with the adaptive training, versus 31.0% correct for nonadaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance. PMID:21060630

Foor, W E; Neifeld, M A

1995-11-10

209

Adaptive, optical, radial basis function neural network for handwritten digit recognition  

NASA Astrophysics Data System (ADS)

An adaptive, optical, radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially multiplexed system that incorporates an on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel by using dual vector-matrix multipliers and a contrast-reversing spatial light modulator. Software is used to emulate an electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7% correct out of 300 testing samples is achieved with the adaptive training, versus 31.0% correct for nonadaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance.

Foor, Wesley E.; Neifeld, Mark A.

1995-11-01

210

Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns  

SciTech Connect

Laser scatter has shown promise as a method to characterize damage microstructural variations as well as a method to characterize surfaces in optical translucent ceramics. Because large volumes of data need to be handled (and sorted) quickly, automated pattern recognition methods using neural networks have been implemented to recognize differences in patterns. A He-Ne laser ({lambda}=0.632{mu}) has been used to obtain scatter patterns from hot pressed Si{sub 3}N{sub 4} with various microstructural variations. By use of a backpropagation neural network running on an IBM PC clone 486/33 machine, a correlation was established between subsurface microstructure and position in Si{sub 3}N{sub 4} ball bearings. The data were confirmed by destructive analysis.

Stinson, M.C. [Central Michigan Univ., (United States). Dept. of Computer Science; Lee, O.W. [Univ. of California, Los Angeles, CA (United States). Dept. of Electrical Engineering; Steckenrider, J.S.; Ellingson, W.A. [Argonne National Lab., IL (United States)

1994-09-01

211

Optical techniques for three-dimensional image recognition  

Microsoft Academic Search

We describe several optoelectronic methods based in digital holography for recognition of 3D images. The phase and amplitude of a Fresnel diffraction pattern of a 3D reference object is measured with digital holography. This complex information is compared with that coming from a similar digital hologram of a 3D input scene using correlation techniques. In this way, the method allows

Enrique Tajahuerce; Osamu Matoba; Bahram Javidi

2001-01-01

212

Translation-invariant pattern recognition based on Synfire chains.  

PubMed

Most of current neural network architectures are not suited to recognize a pattern at various displaced positions. This lack seems due to the prevailing neuron model which reduces a neuron's information transmission to its firing rate. With this information code, a neuronal assembly cannot distinguish between different combinations of its entities and therefore fails to represent the fine structure within a pattern. In our approach, the main idea of the correlation theory is accepted that spatial relationships in a pattern should be coded by temporal relations in the timing of action potentials. However, we do not assume that synchronized spikes are a sign for strong synapses between the neurons concerned. Instead, the synchronization of Synfire chains can be exploited to produce the relevant timing relationships between the neuronal signals. Therefore, we do not require fast synaptic plasticity to account for the precise timing of action potentials. In order to illustrate this claim, we propose a model for translation-invariant pattern recognition which does not depend on any changes in synaptic efficacies. PMID:10420569

Arnoldi, H M; Englmeier, K H; Brauer, W

1999-06-01

213

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

ERIC Educational Resources Information Center

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…

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

2006-01-01

214

Pattern-Recognition System for Approaching a Known Target  

NASA Technical Reports Server (NTRS)

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 lander.

Huntsberger, Terrance; Cheng, Yang

2008-01-01

215

An assessment of carcinogenicity of N-nitroso compounds by the SIMCA method of pattern recognition  

SciTech Connect

The ability to predict the toxic responses of potential environmental pollutants on the basis of their physiochemical properties has many advantages. Pattern recognition methods can be used to predict such pharmacological properties. In this report the SIMCA method of pattern recognition is used to predict the carcinogenicity of N-nitroso compounds, and the advantages of this method of pattern recognition in such applications are discussed.

Dunn, W.J.; Wold, S.

1981-02-01

216

Patterned Condensation Figures as Optical Diffraction Gratings  

Microsoft Academic Search

Heterogeneous, patterned surfaces comprising well-defined hydrophobic and hydrophilic regions and having micrometer-scale periodicities were prepared by patterning the adsorption of omega-functionalized alkanethiolates in self-assembled monolayers (SAMs) on gold. Condensation of water on such surfaces resulted in drops that followed the patterns in the SAMs. These patterned condensation figures (CFs) acted as optical diffraction gratings for reflected (or transmitted) light from

Amit Kumar; George M. Whitesides

1994-01-01

217

Statistical learning theory and its application to pattern recognition  

NASA Astrophysics Data System (ADS)

The problem of pattern recognition is formulated as a classification in the statistic learning theory. Vapnik constructed a class of learning algorithms called support vector machine (SMV) to solve the problem. The algorithm not only has strong theoretical foundation but also provides a powerful tool for solving real-life problems. But it still has some drawbacks. Tow of them are 1) the computational complexity of finding the optimal separating hyperplane is quite high in the linearly separable case, and 2) in the linearly non-separable case, for any given sample set it's hard to choose a proper nonlinear mapping (kernel function) such that the sample set is linearly separable in the new space after the mapping. To overcome these drawbacks, we presented some new approaches. The main idea and some experimental results of the approaches are presented.

Zhang, Ling; Zhang, Bo

2001-09-01

218

Beyond pattern recognition: NOD-like receptors in dendritic cells  

PubMed Central

Innate instruction of adaptive immunity was proposed more than 20 years ago as a mechanism by which long-lived lymphocyte responses are targeted to appropriate antigens. At the time Charles Janeway proposed this theory, most of the innate immune receptors were unknown and the pivotal role of the dendritic cell in instructing T cell priming was debated. There is now overwhelming evidence that the innate and adaptive branches of the immune system must interact to generate immunity. Much of this work has focused on families of innate immune receptors called pattern recognition receptors (PRRs) on dendritic cells, which translate these inflammatory triggers into productive T cell responses. Nevertheless, we are only beginning to understand how these defense molecules shape the generation of immunity. We review the varied roles of one class of PRRs, the NOD-like receptors (NLRs), in immune responses and propose a new model in which adaptive immunity requires coordinated PRR activation within the dendritic cell.

Krishnaswamy, Jayendra Kumar; Chu, Thach; Eisenbarth, Stephanie C.

2013-01-01

219

Using Decision Trees for Comparing Pattern Recognition Feature Sets  

SciTech Connect

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.

Proctor, D D

2005-08-18

220

Recognition of lipopolysaccharide pattern by TLR4 complexes  

PubMed Central

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.

Park, Beom Seok; Lee, Jie-Oh

2013-01-01

221

New Digital Architecture of CNN for Pattern Recognition  

NASA Astrophysics Data System (ADS)

The paper deals with the design of a new digital CNN (Cellular Neural Network) architecture for pattern recognition. The main parameters of the new design were the area consumption of the chip and the speed of calculation in one iteration. The CNN was designed as a digital synchronous circuit. The largest area of the chip belongs to the multiplication unit. In the new architecture we replaced the parallel multiplication unit by a simple AND gate performing serial multiplication. The natural property of this method of multiplication is rounding. We verified some basic properties of the proposed CNN such as edge detection, filling of the edges and noise removing. At the end we compared the designed network with other two CNNs. The new architecture allows to save till 86% gates in comparison with CNN with parallel multipliers.

Raschman, Emil; Záluský, Roman; ?ura?ková, Daniela

2010-07-01

222

Biological agent detection and identification using pattern recognition  

NASA Astrophysics Data System (ADS)

This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and other, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-speciific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncatalogued agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Transue, Kevin D.

2005-05-01

223

Pattern recognition receptors and the inflammasome in kidney disease.  

PubMed

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. PMID:24890433

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

2014-07-01

224

Automatic target recognition using a feature-based optical neural network  

NASA Technical Reports Server (NTRS)

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Chao, Tien-Hsin

1992-01-01

225

Asymptotic analysis of pattern-theoretic object recognition  

NASA Astrophysics Data System (ADS)

Automated target recognition (ATR) is a problem of great importance in a wide variety of applications: from military target recognition to recognizing flow-patterns in fluid- dynamics to anatomical shape-studies. The basic goal is to utilize observations (images, signals) from remote sensors (such as videos, radars, MRI or PET) to identify the objects being observed. In a statistical framework, probability distributions on parameters representing the object unknowns are derived an analyzed to compute inferences (please refer to [1] for a detailed introduction). An important challenge in ATR is to determine efficient mathematical models for the tremendous variability of object appearance which lend themselves to reasonable inferences. This variation may be due to differences in object shapes, sensor-mechanisms or scene- backgrounds. To build models for object variabilities, we employ deformable templates. In brief, the object occurrences are described through their typical representatives (called templates) and transformations/deformations which particularize the templates to the observed objects. Within this pattern-theoretic framework, ATR becomes a problem of selecting appropriate templates and estimating deformations. For an object (alpha) (epsilon) A, let I(alpha ) denote a template (for example triangulated CAD-surface) and let s (epsilon) S be a particular transformation, then denote the transformed template by sI(alpha ). Figure 1 shows instances of the template for a T62 tank at several different orientations. For the purpose of object classification, the unknown transformation s is considered a nuisance parameter, leading to a classical formulation of Bayesian hypothesis- testing in presence of unknown, random nuisance parameters. S may not be a vector-space, but it often has a group structure. For rigid objects, the variation in translation and rotation can be modeled through the action of special Euclidean group SE(n). For flexible objects, such as anatomical shapes, higher-dimensional groups such as a diffeomorphisms are utilized.

Cooper, Matthew L.; Srivastava, Anuj

2000-08-01

226

Pattern recognition techniques and the measurement of solar magnetic fields  

NASA Astrophysics Data System (ADS)

Measuring vector magnetic fields in the solar atmosphere using the profiles of the Stokes parameters of polarized spectral lines split by the Zeeman effect is known as Stokes Inversion. This inverse problem is usually solved by least-squares fitting of the Stokes profiles. However least-squares inversion is too slow for the new generation of solar instruments (THEMIS, SOLIS, Solar-B, ...) which will produce an ever-growing flood of spectral data. The solar community urgently requires a new approach capable of handling this information explosion, preferably in real-time. We have successfully applied pattern recognition and machine learning techniques to tackle this problem. For example, we have developed PCA-inversion, a database search technique based on Principal Component Analysis of the Stokes profiles. Search is fast because it is carried out in low dimensional PCA feature space, rather than the high dimensional space of the spectral signals. Such a data compression approach has been widely used for search and retrieval in many areas of data mining. PCA-inversion is the basis of a new inversion code called FATIMA (Fast Analysis Technique for the Inversion of Magnetic Atmospheres). Tests on data from HAO's Advanced Stokes Polarimeter show that FATIMA isover two orders of magnitude faster than least squares inversion. Initial tests on an alternative code (DIANNE - Direct Inversion based on Artificial Neural NEtworks) show great promise of achieving real-time performance. In this paper we present the latest achievements of FATIMA and DIANNE, two powerful examples of how pattern recognition techniques can revolutionize data analysis in astronomy.

Lopez Ariste, Arturo; Rees, David E.; Socas-Navarro, Hector; Lites, Bruce W.

2001-11-01

227

Vortex induced rotation dynamics of optical patterns.  

PubMed

We demonstrate that modulation instability leading to optical pattern formation can arise by using nonconventional counterpropagating beams carrying an orbital angular momentum (optical vortices). Such a vortex beam is injected into a nonlinear single feedback system. We evidence different complex patterns with peculiar phase singularities and rotating dynamics. We prove that the dynamics is induced by the vortex angular momentum and the rotation velocity depends nonlinearly on both the vortex topological charge and the intensity of the input beam. PMID:23004980

Caullet, V; Marsal, N; Wolfersberger, D; Sciamanna, M

2012-06-29

228

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

PubMed

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. PMID:24939367

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

2014-01-01

229

Pattern recognition in geochemical hydrocarbon exploration: a fuzzy approach  

SciTech Connect

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.

Granath, G.

1988-08-01

230

Relative performance evaluation of pattern recognition models for nondestructive damage detection  

NASA Astrophysics Data System (ADS)

The objective of this paper is to evaluate the relative performance of several Bayesian distance-based pattern recognition models and two non-Bayesian models for non-destructive damage detection (NDD). A theory of damage localization, which yields information on the location of the damage directly from changes in mode shapes, is formulated. Next, the application of pattern recognition for NDD is established. Expressions for pattern classification using discriminate functions based on Bayes' Rule, Neyman-Pearson criteria, and neural networks are generated. A set of criteria for the evaluation of the pattern recognition models is then established. Damage localization is applied to a finite element mode of a structure which contains simulated damage at various locations using the pattern recognition and neural network models. Finally, the evaluation of the pattern recognition models is carried out using the established criteria.

Garcia, Gabriel V.; Stubbs, Norris; Butler, Karen

1996-04-01

231

Arrogance analysis of several typical pattern recognition classifiers  

NASA Astrophysics Data System (ADS)

Various kinds of classification methods have been developed. However, most of these classical methods, such as Back-Propagation (BP), Bayesian method, Support Vector Machine(SVM), Self-Organizing Map (SOM) are arrogant. A so-called arrogance, for a human, means that his decision, which even is a mistake, overstates his actual experience. Accordingly, we say that he is a arrogant if he frequently makes arrogant decisions. Likewise, some classical pattern classifiers represent the similar characteristic of arrogance. Given an input feature vector, we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Typically, for a new sample which is distinguishable from original training samples, traditional classifiers recognize it as one of the known targets. Clearly, arrogance in classification is an undesirable attribute. Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. Inquisitiveness is, in many ways, the opposite of arrogance. In nature, inquisitiveness is an eagerness for knowledge characterized by the drive to question, to seek a deeper understanding. The human capacity to doubt present beliefs allows us to acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the constructive investigation and exploitation of conflict in information. Thus, we quantify this balance and discuss new techniques that will detect arrogance in a classifier.

Jing, Chen; Xia, Shengping; Hu, Weidong

2007-04-01

232

PRoNTo: pattern recognition for neuroimaging toolbox.  

PubMed

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. PMID:23417655

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

233

Distributed Output Encoding for Multi-Class Pattern Recognition  

Microsoft Academic Search

Fingerspelling recognition and handshape recognition are two examples of real-world, multi-class recognition problems consisting of 26 and 78 classes respectively. While it is theoretically possible to solve any multi-class problem with a single “smart” classifier the complexity of such a classifier is usually prohibitively high. This paper looks at several approaches to solving a numerous multi-class recognition problem and discusses

Roman Erenshteyn; Pavel Laskov; David M. Saxe; Richard A. Foulds

1999-01-01

234

Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers  

Microsoft Academic Search

The classification and recognition of two-dimensional patterns independently of their position, orientation, and size by using high-order networks are discussed. A method is introduced for reducing and controlling the number of weights of a third-order network used for invariant pattern recognition. The method leads to economical networks that exhibit high recognition rates for translated, rotated, and scaled, as well as

Stavros J. Perantonis; P. J. G. Lisboa

1992-01-01

235

The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network  

Microsoft Academic Search

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

Gail A. Carpenter; Stephen Grossberg

1988-01-01

236

A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control  

Microsoft Academic Search

Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of ~95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited

Ann M. Simon; Levi J. Hargrove; Blair A. Lock; Todd A. Kuiken

2009-01-01

237

Research on fault pattern recognition for aircraft fuel system with its performance simulation  

Microsoft Academic Search

This paper presents application research on fault pattern recognition for aircraft fuel system based on its performance simulation. Fault pattern recognition method is used to perform fault detection, fault isolation, fault prognostics and so on in complicated system, which is basic research contents of prognostics and health management (PHM). Since the hardware of fuel system for an aircraft is too

Haifeng Wang; Bifeng Song; Fangyi Wan

2011-01-01

238

Microphotonic header-recognition architecture for high-speed optical networks  

Microsoft Academic Search

We demonstrate a MicroPhotonic optical header-recognition architecture based on delaying the optical headers within an optical cavity and detecting them using a photoreceiver array. The MicroPhotonic architecture overcomes the issue of optical interference encountered in conventional single-detector header-recognition structures. A 4-bit MicroPhotonic header-recognition architecture is simulated and experimentally demonstrated at 200 Mb\\/s

Muhsen Aljada; Kamal E. Alameh; Khalid Al-Begain

2006-01-01

239

Dense pattern optical multipass cell  

DOEpatents

A multiple pass optical cell and method comprising providing a pair of opposed cylindrical mirrors having curved axes with substantially equal focal lengths, positioning an entrance hole for introducing light into the cell and an exit hole for extracting light from the cell, wherein the entrance hole and exit hole are coextensive or non-coextensive, introducing light into the cell through the entrance hole, and extracting light from the cell through the exit hole.

Silver, Joel A [Santa Fe, NM

2009-01-13

240

Dense Pattern Optical Multipass Cell  

NASA Technical Reports Server (NTRS)

A multiple pass optical cell and method comprising providing a pair of opposed cylindrical mirrors having curved axes with substantially equal focal lengths, positioning an entrance hole for introducing light into the cell and an exit hole for extracting light from the cell, wherein the entrance hole and exit hole are coextensive or non-coextensive, introducing light into the cell through the entrance hole, and extracting light from the cell through the exit hole.

Silver, Joel A. (Inventor)

2009-01-01

241

Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division  

PubMed Central

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

Loo, Chu Kiong

2014-01-01

242

Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division.  

PubMed

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach. PMID:24883361

Yousefi, Bardia; Loo, Chu Kiong

2014-01-01

243

Magnetic resonance imaging pattern recognition in hypomyelinating disorders.  

PubMed

Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T(2)-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T(2) lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus-Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus-Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis. PMID:20881161

Steenweg, Marjan E; Vanderver, Adeline; Blaser, Susan; Bizzi, Alberto; de Koning, Tom J; Mancini, Grazia M S; van Wieringen, Wessel N; Barkhof, Frederik; Wolf, Nicole I; van der Knaap, Marjo S

2010-10-01

244

Magnetic resonance imaging pattern recognition in hypomyelinating disorders  

PubMed Central

Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus–Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T2-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T2 lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus–Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus–Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus–Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis.

Steenweg, Marjan E.; Vanderver, Adeline; Blaser, Susan; Bizzi, Alberto; de Koning, Tom J.; Mancini, Grazia M. S.; van Wieringen, Wessel N.; Barkhof, Frederik; Wolf, Nicole I.

2010-01-01

245

Infrared target simulation environment for pattern recognition applications  

NASA Astrophysics Data System (ADS)

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.

Savakis, Andreas E.; George, Nicholas

1994-07-01

246

A pyramidal neural network for visual pattern recognition.  

PubMed

In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient back-propagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM). PMID:17385623

Phung, Son Lam; Bouzerdoum, Abdesselam

2007-03-01

247

Role of pattern recognition receptors in flavivirus infections.  

PubMed

The flaviviral encephalitis has now become a major health concern in global scale. The efficient detection of viral infection and induction of the innate antiviral response by host's innate immune system are crucial to determine the outcome of infection. The intracellular pattern recognition receptors TLRs, RLRs, NLRs and CLRs play a central role in detection and initiation of robust antiviral response against flaviviral infection. Both cytoplasmic RLRs, RIG-I and MDA5 have been shown to be implicated in sensing flaviviral genomic RNA. Similarly among TLRs mainly TLR3 and TLR7 are known to respond in flaviviral infections as they are known to sense dsRNA and ssRNA moiety as their natural cognate ligand. Several studies have also shown the roles of NLRs and CLRs in mounting an innate antiviral response against flavivirus but, it is yet to be completely understood. Until now only few reports have implicated NLRs and CLRs in induction of antiviral and proinflammatory state following flaviviral infection. The current review therefore aims to comprehensively analyze past as well as current understanding on the role of PRRs in flaviviral infections. PMID:24657789

Nazmi, Arshed; Dutta, Kallol; Hazra, Bibhabasu; Basu, Anirban

2014-06-24

248

A neural network based model for abnormal pattern recognition of control charts  

Microsoft Academic Search

In the past years, artificial neural networks were used for pattern recognition of control charts with an emphasis on recognizing specific abnormal patterns of control charts. This paper proposes an artificial neural network based model, which contains several back propagation networks, to both recognize the abnormal control chart patterns and estimate the parameters of abnormal patterns such as shift magnitude,

Ruey-Shy Guh; Yi-Chih Hsieh

1999-01-01

249

A new phase pattern recognition tool applied to field line resonances  

NASA Astrophysics Data System (ADS)

The detection and characterization of geomagnetic pulsations (standing Alfven waves on magnetospheric field lines, as produced by the field-line resonance (FLR) process) using ground magnetic field data has been based for decades on the interpretation of the longitudinal and latitudinal distributions of pulsation amplitudes and phases. By adopting this approach only clear and single FLRs can be correctly analyzed. Magnetometer array data, however, contain much more phase information due to the coherency of the ground observed FLR wave structures across the array of stations, which remains undisclosed if phase pattern recognition of beamforming techniques are not used. We present theory and applications of such a new phase pattern recognition tool, the Field-Line Resonance Detector (FLRD), which is an adaptation of the wave telescope technique, previously used in seismology and multi-spacecraft analysis. Unlike the traditional methods the FLRD is able to detect and fully characterize multiple superposed or hidden FLR structures, of which the tool allows for an automated detection. We show results of its application in a statistical analysis of one year (2002) of ground magnetometer data from the Canadian magnetometer array CANOPUS (now known as CARISMA, www.carisma.ca) and a comparison of FLRD results with other ground-based data from optical and radar instruments. The remarkable adaptability of the tool to other datasets and phase structures shall also be discussed.

Plaschke, F.; Glassmeier, K.-H.; Milan, S. E.; Mann, I. R.; Motschmann, U.; Rae, I. J.

2009-04-01

250

Pattern recognition analysis for 1 H NMR spectra of plasma from hemodialysis patients  

Microsoft Academic Search

1H NMR spectroscopic and pattern recognition-based methods (NMR-PR) were applied to the metabolic profiling studies on hemodialysis\\u000a (HD). Plasma samples were collected from 37 patients before and after HD and measured by 600 MHz NMR spectroscopy. Each spectrum\\u000a was data-processed and subjected to principal component analysis for pattern recognition. Spectral patterns of plasma between\\u000a pre- and post-dialyses were clearly discriminated, together

Masako Fujiwara; Takeshi Kobayashi; Takahiro Jomori; Yutaka Maruyama; Yoshitomo Oka; Hiroshi Sekino; Yutaka Imai; Kazuhisa Takeuchi

2009-01-01

251

Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation.  

PubMed

Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent control. Finally, patients qualitatively preferred using pattern recognition control and reported the resulting control to be smoother and more consistent. PMID:24110008

Hargrove, Levi J; Lock, Blair A; Simon, Ann M

2013-01-01

252

Traffic Sign Detection and Pattern Recognition Using Support Vector Machine  

Microsoft Academic Search

A vision based vehicle guidance system must be able to detect and recognize traffic signs. Traffic sign recognition systems collect information about road signs and helps the driver to make timely decisions, making driving safer and easier. This paper deals with the detection and recognition of traffic signs from image sequences using the colour information. Colour based segmentation techniques are

C. G. Kiran; Lekhesh V. Prabhu; V. Abdu Rahiman; K. Rajeev

2009-01-01

253

Biometrics: Proving Ground for Image and Pattern Recognition  

Microsoft Academic Search

The emerging requirements of reliable and highly accurate personal identification in a number of government and commercial applications (e.g., international border crossings, access to buildings, laptops and mobile phones) have served as an impetus for a tremendous growth in biometric recognition technology. Biometrics refers to the automatic recognition of an individual by using anatomical or behavioral traits associated with that

A. K. Jain

2007-01-01

254

An assessment of the carcinogenicity of N-nitroso compounds by the SIMCA method of pattern recognition  

Microsoft Academic Search

The ability to predict the toxic responses of potential environmental pollutants on the basis of their physiochemical properties has many advantages. Pattern recognition methods can be used to predict such pharmacological properties. In this report the SIMCA method of pattern recognition is used to predict the carcinogenicity of N-nitroso compounds, and the advantages of this method of pattern recognition in

William J. Dunn; Svante Wold

1981-01-01

255

[Study on the methods and applications of near-infrared spectroscopy chemical pattern recognition].  

PubMed

Near infrared spectroscopy pattern recognition technique is an important part of modern near infrared spectroscopy technique. In the present paper, main methods of near infrared spectroscopy chemical pattern recognition and some recent developments are introduced. Basic principles of the methods in cluster analysis, discriminant analysis and latent projection are discussed, including some new methods such as support vector machines (SVM), bubble agglomeration algorithm (BA) and focal eigen functions (FEF) etc. Finally, the applications of near infrared spectroscopy pattern recognition technique in agriculture, pharmaceutical industry, food analysis, petroleum industry and other fields are reviewed. PMID:17944399

Li, Yan-Zhou; Min, Shun-Geng; Liu, Xia

2007-07-01

256

Pattern recognition using versatile hybrid joint transform correlators: some techniques for improving the performance  

NASA Astrophysics Data System (ADS)

This paper reports the outcome of research studies carried out by us in the area of Optical Pattern Recognition. A hybrid JTC architecture has been used to evaluate correlation performance of four different types of JTCs in a non-cooperative situation. A non-zero order JTC has been proposed based on the principle of differential processing of joint power spectrum and its sensitivity to illumination variation has been investigated. A hybrid wavelet transform based JTC has been proposed and demonstrated to achieve high image discrimination. Hartley transform has been introduced in joint-transform correlation. Chirp modulation in HJTC has been demonstrated to extract information on object correlation and absolute object position from correlation output. A discrimination sensitive rotation-invariant JTC has been proposed based on gradient preprocessing and circular harmonic decomposition.

Pati, G. S.; Singh, Kehar

1999-04-01

257

Multiresolution pattern recognition of small volcanos in Magellan data  

NASA Astrophysics Data System (ADS)

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 105 to 106 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.

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

1992-12-01

258

Hand posture recognition using jointly optical flow and dimensionality reduction  

NASA Astrophysics Data System (ADS)

Hand posture recognition is generally addressed by using either YC b C r (luminance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished from the background from some colorfulness and luminance properties. This can hardly be used when a dark hand, or a hand of any color, is under study. In addition, existing recognition processes rely on descriptors or geometric shapes which can be reliable; this comes at the expense of an increased computational complexity. To cope with these drawbacks, this paper proposes a four-step method recognition technique consisting of (i) a pyramidal optical flow for the detection of large movements and hence determine the region of interest containing the expected hand, (ii) a preprocessing step to compute the hand contour while ensuring geometric and illumination invariance, (iii) an image scanning method providing a signature which characterizes non-star-shaped contours with a one-pixel precision, and (iv) a posture classification method where a sphericity criterion preselects a set of candidate postures, principal component analysis reduces the dimensionality of the data, and Mahalanobis distance is used as a criterion to identify the hand posture in any test image. The proposed technique has been assessed in terms of its performances including the computational complexity using both visual and statistical results.

Boughnim, Nabil; Marot, Julien; Fossati, Caroline; Bourennane, Salah

2013-12-01

259

Human actions recognition using bag of optical flow words  

NASA Astrophysics Data System (ADS)

In this paper, we present an improved approach to recognize human action based on the BOW model and the pLSA model. We propose an improved feature with optical flow to build our bag of words. This feature is able to reduce the high dimension of the pure optical flow template and also has abundant motion information. Then, we use the topic model of pLSA (probabilistic Latent Semantic Analysis) to classify human actions in a special way. We find that the existing methods lead to some mismatching of words due to the k-means clustering approach. To reduce the probability of mismatching, we add the spatial information to each word and improve the training and testing approach. Our approach of recognition is tested on two datasets, the KTH datasets and WEIZMANN datasets. The result shows its good performance.

Zhang, Xu; Miao, Zhenjiang; Wan, Lili

2012-04-01

260

New Features Extraction Method for People Recognition on the Basis of the Iris Pattern  

Microsoft Academic Search

Biometric people recognition methods are increasingly popular, yet there is no biometric authentication standard used in everyday life. Despite a lot of work on biometric people recognition methods, especially those based on the iris pattern, which is the subject of the author's research, there is still room for designing a new, optimal method, e.g. one that would be simpler in

R. Szewczyk

2007-01-01

261

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

PubMed Central

Galectins constitute an evolutionary conserved family of ?-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.

Vasta, Gerardo R.

2012-01-01

262

Spray Pattern Recognition for Multi-Hole Gasoline Direct Injectors Using CFD Modeling  

Microsoft Academic Search

This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and

Sudhakar Das; Shi-Ing Chang; John Kirwan

263

Design of MultiMode Switch Strategy for Lean Burn Engine Using Driving Pattern Recognition Technique  

Microsoft Academic Search

A multi-mode switch strategy based on driving pattern recognition (DPR) technique used for lean burn engine is proposed in this paper. First, four representative driving patterns (RDP) are selected from nine Taiwan driving patterns. The single-mode switch strategy is then extracted and optimized for each RDP by analyzing the results of dynamic programming. The proposed strategy can select the appropriate

Bo-Chiuan Chen; Yuh-Yih Wu; Feng-Chi Hsieh

2006-01-01

264

Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection.  

National Technical Information Service (NTIS)

In this work, four data sets representing typical chemical sensor array data were used to compare seven pattern recognition algorithms nearest neighbor, Mahalanobis linear discriminant analysis, Bayesian linear discriminant analysis, SIMCA, back propagati...

R. Shaffer S. L. Rose-Pehrsson R. A. McGill

1998-01-01

265

Patient training for functional use of pattern recognition-controlled prostheses  

PubMed Central

Pattern recognition control systems have the potential to provide better, more reliable myoelectric prosthesis control for individuals with an upper-limb amputation. However, proper patient training is essential. We begin user training by teaching the concepts of pattern recognition control and progress to teaching how to control, use, and maintain prostheses with one or many degrees of freedom. Here we describe the training stages, with relevant case studies, and highlight several tools that can be used throughout the training process, including prosthesis-guided training (PGT)—a self-initiated, simple method of recalibrating a pattern recognition–controlled prosthesis. PGT may lengthen functional use times, potentially increasing prosthesis wear time. Using this training approach, we anticipate advancing pattern recognition control from the laboratory to the home environment and finally realizing the full potential of these control systems.

Simon, Ann M.; Lock, Blair A.; Stubblefield, Kathy A.

2012-01-01

266

Comparison of SIMCA Pattern Recognition and Library Search Identification of Hazardous Compounds from Mass Spectra.  

National Technical Information Service (NTIS)

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. A scheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocar...

M. Sarker W. G. Glen L. B. Yin W. J. Dunn D. R. Scott

1990-01-01

267

Patient training for functional use of pattern recognition-controlled prostheses.  

PubMed

Pattern recognition control systems have the potential to provide better, more reliable myoelectric prosthesis control for individuals with an upper-limb amputation. However, proper patient training is essential. We begin user training by teaching the concepts of pattern recognition control and progress to teaching how to control, use, and maintain prostheses with one or many degrees of freedom. Here we describe the training stages, with relevant case studies, and highlight several tools that can be used throughout the training process, including prosthesis-guided training (PGT)-a self-initiated, simple method of recalibrating a pattern recognition-controlled prosthesis. PGT may lengthen functional use times, potentially increasing prosthesis wear time. Using this training approach, we anticipate advancing pattern recognition control from the laboratory to the home environment and finally realizing the full potential of these control systems. PMID:22563231

Simon, Ann M; Lock, Blair A; Stubblefield, Kathy A

2012-04-01

268

COMPARISON OF SIMCA PATTERN RECOGNITION AND LIBRARY SEARCH IDENTIFICATION OF HAZARDOUS COMPOUNDS FROM MASS SPECTRA  

EPA Science Inventory

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. cheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons, and poly...

269

COMPARISON OF SIMCA PATTERN RECOGNITION & LIBRARY SEARCH IDENTIFICATION OF HAZARDOUS COMPOUNDS FROM MASS SPECTRA  

EPA Science Inventory

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. cheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons, and poly...

270

Pattern Recognition Analysis of a Set of Mutagenic Aliphatic N-Nitrosamines.  

National Technical Information Service (NTIS)

A set of 21 mutagenic aliphatic N-nitrosamines were subjected to a pattern recognition analysis using ADAPT software. Four descriptors based on molecular connectivity, geometry and sigma charge on nitrogen were capable of achieving a 100% classification u...

S. Nesnow R. Langenbach M. J. Mass

1985-01-01

271

Pattern Recognition of EEG-EOG as a Technique for All-Night Sleep Stage Scoring.  

National Technical Information Service (NTIS)

The study reports an approach to using a pattern recognition system, simulated on a digital computer, for automatic, all-night sleep stage scoring. A detailed comparison of percent agreement for the computer and three experienced human judges is presented...

W. B. Martin S. S. Viglione R. D. Joseph L. C. Johnson P. Naitoh

1971-01-01

272

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

NASA Technical Reports Server (NTRS)

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.

Guseman, L. F., Jr.

1983-01-01

273

Application of extension theory to PD pattern recognition in high-voltage current transformers  

Microsoft Academic Search

This paper presents a novel partial-discharge (PD) recognition method based on the extension theory for high-voltage cast-resin current transformers (CRCTs). First, a commercial PD detector is used to measure the three-dimensional (3-D) PD patterns of the high-voltage CRCTs, then three data preprocessing schemes that extract relevant features from the raw 3-D-PD patterns are presented for the proposed PD recognition method.

Mang-Hui Wang; Chih-Yung Ho

2005-01-01

274

Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area  

Microsoft Academic Search

Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the\\u000a fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with\\u000a DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization\\u000a method reflects more efficiently the fuzzy

Yuan-yuan Mao; Xue-gang Zhang; Lian-sheng Wang

2006-01-01

275

Flat foot functional evaluation using pattern recognition of ground reaction data  

Microsoft Academic Search

Objective. Main purpose of this study was to apply quantitative gait analysis and statistical pattern recognition as clinical decision-making aids in flat foot diagnosis and post-surgery monitoring.Design. Statistical pattern recognition techniques were applied to discriminate between normal and flat foot populations through ground reaction force measurements; ground reaction forces time course was assumed as a sensible index of the foot

A. Bertani; A. Cappello; M. G. Benedetti; L. Simoncini; F. Catani

1999-01-01

276

Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.  

PubMed

Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This paper focused on investigating the optimal interelectrode distance, channel configuration, and electromyography feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p < 0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p < 0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a linear discriminant analysis classifier and found that an autoregressive set significantly (p < 0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set. PMID:22147289

Young, Aaron J; Hargrove, Levi J; Kuiken, Todd A

2012-03-01

277

A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses  

PubMed Central

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.

Powell, Michael A.; Thakor, Nitish V.

2012-01-01

278

Finger vein recognition using weighted local binary pattern code based on a support vector machine  

Microsoft Academic Search

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It\\u000a is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located\\u000a inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern\\u000a (LBP) and

Hyeon Chang Lee; Byung Jun Kang; Eui Chul Lee; Kang Ryoung Park

2010-01-01

279

Feature-based pattern recognition and object identification for telerobotics  

Microsoft Academic Search

This paper 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, the characteristic model image features are extracted during preprocessing. Feature vectors representing

Jae-Kyu Lee; G. F. Mauer

2005-01-01

280

Shape Variation-Based Frieze Pattern for Robust Gait Recognition  

Microsoft Academic Search

Gait is an attractive biometric for vision-based human identification. Previous work on existing public data sets has shown that shape cues yield improved recognition rates compared to pure motion cues. However, shape cues are fragile to gross appearance variations of an individual, for example, walking while carrying a ball or a backpack. We introduce a novel, spatiotemporal Shape Variation-Based Frieze

Seungkyu Lee; Yanxi Liu; Robert T. Collins

2007-01-01

281

Semi-supervised kernel learning based optical image recognition  

NASA Astrophysics Data System (ADS)

This paper is to propose semi-supervised kernel learning based optical image recognition, called Semi-supervised Graph-based Global and Local Preserving Projection (SGGLPP) through integrating graph construction with the specific DR process into one unified framework. SGGLPP preserves not only the positive and negative constraints but also the local and global structure of the data in the low dimensional space. In SGGLPP, the intrinsic and cost graphs are constructed using the positive and negative constraints from side-information and k nearest neighbor criterion from unlabeled samples. Moreover, kernel trick is applied to extend SGGLPP called KSGGLPP by on the performance of nonlinear feature extraction. Experiments are implemented on UCI database and two real image databases to testify the feasibility and performance of the proposed algorithm.

Li, Jun-Bao; Yang, Zhi-Ming; Yu, Yang; Sun, Zhen

2012-08-01

282

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

PubMed Central

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.

Swartz, R. Andrew

2013-01-01

283

COMPUTER TECHNOLOGY: PATTERN RECOGNITION OF BEHAVIORAL EVENTS IN THE NONHUMAN PRIMATE  

EPA Science Inventory

Techniques used in computer graphics and pattern analysis have been applied to the tasks of observing, classifying, and recording spontaneous behavioral activities in the captive primate. The goal in designing this system was to provide a computer-based pattern recognition system...

284

Control of a Hybrid Electric Truck Based on Driving Pattern Recognition  

Microsoft Academic Search

The design procedure of a multi-mode power management control strategy with driving pattern recognition is proposed. The design goal of the control strategy is to minimize fuel consumption and engine-out NOx and PM emissions on diversified driving schedules. Six representative driving patterns (RDP) are designed based on the driving characteristics to represent different driving scenarios. For each RDP, the Dynamic

Chan-Chiao Lin; Huei Peng; Soonil Jeon; Jang Moo Lee

2002-01-01

285

Pattern recognition in a compartmental model of a CA1 pyramidal neuron  

Microsoft Academic Search

Computer simulation of a CA1 hippocampal pyramidal neuron is used to estimate the effects of synaptic and spatio-temporal noise on such a cell's ability to accurately calculate the weighted sum of its inputs, presented in the form of transient patterns of activity. Comparison is made between the pattern recognition capability of the cell in the presence of this noise and

Bruce P Graham

2001-01-01

286

Human Activity Recognition and Pathological Gait Pattern Identification  

Microsoft Academic Search

Human activity analysis has attracted great interest from computer vision researchers due to its promising applications in many areas such as automated visual surveillance, computer-human interactions, and motion-based identification and diagnosis. This dissertation presents work in two areas: general human activity recognition from video, and human activity analysis for the purpose of identifying pathological gait from both 3D captured data

Feng Niu

2007-01-01

287

Patterns of codon recognition by isoacceptor aminoacyl-tRNAs from wheat germ.  

PubMed Central

Isoacceptors of Ala-, Arg-, Glu-, Gln-, Ile-, Leu-, Lys-, Ser-, Thr- and Val-tRNAs from wheat germ have been resolved by reverse phast chromatography. Codon recognition properties have been determined on isolated fractions of each of these aa-tRNAs and codon assignments have been made to a number of isoacceptors. Evolutionary changes which have occurred in patterns of codon recognition by isoacceptor aa-tRNAs in wheat germ and other organisms are discussed.

Hatfield, D; Rice, M

1978-01-01

288

A hybrid system for SPC concurrent pattern recognition  

Microsoft Academic Search

Any nonrandom patterns shown in Statistical Process Control (SPC) charts imply possible assignable causes that may deteriorate the process performance. Hence, timely detecting and recognizing Control Chart Patterns (CCPs) for nonrandomness is very important in the implementation of SPC. Due to the limitations of run-rule-based approaches, Artificial Neural Networks (ANNs) have been resorted for detecting CCPs. However, most of the

Zheng Chen; Susan Lu; Sarah Lam

2007-01-01

289

A Tree System Approach for Fingerprint Pattern Recognition  

Microsoft Academic Search

The purpose of this paper is to demonstrate how a syntactic approach and, in particular, a tree system may be used to represent and classify fingerprint patterns. The fingerprint impressions are subdivided into sampling squares which are preprocessed and postprocessed for feature extraction. A set of regular tree languages is used to describe the fingerprint patterns and a set of

Bijan Moayer; King-sun Fu

1976-01-01

290

A Tree System Approach for Fingerprint Pattern Recognition  

Microsoft Academic Search

The purpose of this paper is to demonstrate how a syntactic approach and, in particular, a tree system may be used to represent and classify fingerprint patterns. The fingerprint impressions are subdivided into sampling squares which are preprocessed and postprocessed for feature extraction. A set of regular tree languages is used to describe the fingerprint patterns and a set of

Bijan Moayer; King-Sun Fu

1986-01-01

291

Innate Pattern Recognition and Categorization in a Jumping Spider  

PubMed Central

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.

Dolev, Yinnon; Nelson, Ximena J.

2014-01-01

292

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits  

NASA Astrophysics Data System (ADS)

We present a new pattern recognition system based on moving average and linear discriminant analysis (LDA), which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.

Gu, Yu; Li, Qiang

2014-04-01

293

Pattern recognition applied to volcanic activity: Identification of the precursory patterns to Etna recent flank eruptions and periods of rest  

NASA Astrophysics Data System (ADS)

Computational pattern recognition is an invaluable tool in understanding the phenomenology of complex processes and represents the first step towards their effective physical modeling. So far it has never been used in volcanology. We discuss in detail pattern recognition algorithms of the "logic" type and present an application to the recent eruptive activity of Mount Etna volcano. The specific aim is a characterization of the intermediate-term precursory patterns to its flank eruptions. A comparatively successful recognition is obtained, providing the combinations of parameters which have been precursory to eruptions and periods of rest in the last fifteen years. The recognized patterns yield two main results: (a) the seismicity in the Gulf of Patti is identified as the most important precursor, and a further correlation study confirms this issue as highly significant, implying that regional tectonic stress, and in particular the structures around the Tindari-Giardini lineament, play a fundamental role in triggering Etna flank activity; (b) an operative prediction-oriented application of the recognized precursory patterns is tentatively possible.

Mulargia, Francesco; Gasperini, Paolo; Marzocchi, Warner

1991-04-01

294

Classifying two-dimensional orbits using pattern recognition  

NASA Astrophysics Data System (ADS)

We present a fast algorithm to identify both regular and irregular orbits that map out a sustained shape in configuration space. The method, which we dub 'pattern autocorrelation' (PACO), detects a repeating pattern in time-series constructed from binary sign changes in phase-space coordinates reduced to two dimensions. This is achieved by computing the autocorrelation function of the time-series, and by retrieving a pattern and a pattern-to-signal ratio. We apply the method to two-dimensional orbits in the logarithmic potential in an application to spiral galaxies with an asymptotically flat rotation curve; the general case of three-dimensional orbits is sketched. We find that irregular orbits can yet sustain the smooth morphological features of a galaxy for a substantial fraction of a Hubble time: this fraction is quantified through the pattern-to-signal ratio. In the case where a central supermassive black hole is added to the potential, we find that up to ?16% of initial conditions space yields irregular motion that may sustain long-lived regular features. The method further detects and distinguishes orbits that are not based on Lissajous theory of resonant motion.

Faber, N. T.; Flitti, F.; Boily, C. M.; Collet, C.; Patsis, P. A.; Portegies Zwart, S.

2013-12-01

295

Optical music recognition on the International Music Score Library Project  

NASA Astrophysics Data System (ADS)

A system is presented for optical recognition of music scores. The system processes a document page in three main phases. First it performs a hierarchical decomposition of the page, identifying systems, staves and measures. The second phase, which forms the heart of the system, interprets each measure found in the previous phase as a collection of non-overlapping symbols including both primitive symbols (clefs, rests, etc.) with fixed templates, and composite symbols (chords, beamed groups, etc.) constructed through grammatical composition of primitives (note heads, ledger lines, beams, etc.). This phase proceeds by first building separate top-down recognizers for the symbols of interest. Then, it resolves the inevitable overlap between the recognized symbols by exploring the possible assignment of overlapping regions, seeking globally optimal and grammatically consistent explanations. The third phase interprets the recognized symbols in terms of pitch and rhythm, focusing on the main challenge of rhythm. We present results that compare our system to the leading commercial OMR system using MIDI ground truth for piano music.

Raphael, Christopher; Jin, Rong

2013-12-01

296

Quantification of feature space changes with experience during electromyogram pattern recognition control.  

PubMed

Pattern recognition of the electromyogram (EMG) has been demonstrated in the laboratory to be a successful alternative to conventional control methods for myoelectric prostheses. Pattern recognition control is dependent upon both machine and user learning; the user learns to generate distinct classes of muscle activity while the machine learns to interpret them. With experience, users may learn to generate distinct classes by reducing intraclass variability or by increasing interclass distance. The goal of this study was to identify which of these strategies best explained differences in EMG patterns between subjects with and without experience using pattern recognition control. We compared classification errors of novice nonamputee subjects with experienced nonamputee subjects. We found that after brief exposure to the control method, classification error in novices was reduced, although not to the level of experienced subjects. While the level of intraclass variability in novices was similar to that of the experienced subjects, they did not achieve the same level of interclass distance. These differences can be used to guide the development of much needed rehabilitation methods to train subjects to use pattern recognition devices. In particular we recommend training protocols that emphasize increasing the interclass distance. PMID:22262686

Bunderson, Nathan E; Kuiken, Todd A

2012-05-01

297

Laser Opto-Electronic Correlator for Robotic Vision Automated Pattern Recognition  

NASA Technical Reports Server (NTRS)

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

Marzwell, Neville

1995-01-01

298

Chromosome region recognition based on local band patterns  

Microsoft Academic Search

To make the visual examination of a chromosome image for various chromosome abnormalities, individual chromosome regions have to be extracted from the image and classified into the distinct chromosome types in advance. To improve the accuracy and flexibility in this process, a subregion (local band pattern) based method has been proposed for recognizing individual chromosome regions in the image. This

Toru Abe; Chieko Hamada; Tetsuo Kinoshita

2008-01-01

299

Pattern recognition computation using action potential timing for stimulus representation  

Microsoft Academic Search

A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one

J. J. Hopfield

1995-01-01

300

Earth Observations: Pattern Recognition of the Earth System  

NSDL National Science Digital Library

Earth Observation is an activity where students observe and patterns in datasets of the different Earth spheres. Correlations between datasets are examined to stimulate student thinking of the interrelations in the Earth system. This activity is connect to process and concepts covered later in the class.

Nyman, Matthew

301

A novel local pattern descriptor-local vector pattern in high-order derivative space for face recognition.  

PubMed

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. PMID:24808409

Fan, Kuo-Chin; Hung, Tsung-Yung

2014-07-01

302

Optical trapping and direct optical patterning of plasmonic nanoparticle assemblies  

NASA Astrophysics Data System (ADS)

This thesis consists of research on two general topics, the correlation between nanostructure and optical response in Au nanoparticle arrays, and the discovery and development of new methods for the all-optical directed assembly of Au nanoparticles on surfaces. In Chapter 3, the plasmonic coupling between Au nanoparticles in 2-dimensional superlattice arrays is probed using two-photon fluorescence. The experimental results are compared with finite-difference time domain (FDTD) simulations. Chapter 4 describes the patterning of surfaces with metal nanoparticles using an optical trapping setup. A precision of placement of approximately 100 nm is observed using this method, which requires no lithography or scanning probes. Finally, in Chapter 5 a new method for the selective deposition of Au bipyramids is presented. Colloidal Au bipyramids are observed to deposit onto glass surfaces when illuminated with near-IR laser light undergoing total internal reflection at the glass-water interface. The mechanism for this deposition is shown to be the selective photothermal heating of Au bipyramids, reducing their aspect ratio in the process.

Guffey, Mason James

303

Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study  

PubMed Central

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical gener ality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

Fang, Hua; Espy, Kimberly Andrews; Rizzo, Maria L.; Stopp, Christian; Wiebe, Sandra A.; Stroup, Walter W.

2010-01-01

304

Contact pattern-induced pair potentials for protein fold recognition.  

PubMed

The protein structure prediction problem is considered as a problem of fitting a sequence into a folding motif. We focus on finding an approximative structure representation providing the best preferences or contact energies. A 2-D structure description in the form of specific contact matrices is used. The main features of our approach are (i) only contacts involved in characteristic interaction patterns are considered, (ii) amino acid pair preferences or contact energies related to these interaction patterns are derived from the structural database and (iii) from the evaluation of individual structure elements, hypotheses on the alignment of a new sequence to a given structure may be derived. Results are demonstrated in particular to examples of the blue copper proteins. PMID:7567919

Selbig, J

1995-04-01

305

Recognition of Higher Order Patterns in Proteins: Immunologic Kernels  

PubMed Central

By applying analysis of the principal components of amino acid physical properties we predicted cathepsin cleavage sites, MHC binding affinity, and probability of B-cell epitope binding of peptides in tetanus toxin and in ten diverse additional proteins. Cross-correlation of these metrics, for peptides of all possible amino acid index positions, each evaluated in the context of a ±25 amino acid flanking region, indicated that there is a strongly repetitive pattern of short peptides of approximately thirty amino acids each bounded by cathepsin cleavage sites and each comprising B-cell linear epitopes, MHC–I and MHC-II binding peptides. Such “immunologic kernel” peptides comprise all signals necessary for adaptive immunologic cognition, response and recall. The patterns described indicate a higher order spatial integration that forms a symbolic logic coordinating the adaptive immune system.

Bremel, Robert D.; Homan, E. Jane

2013-01-01

306

Recognition of higher order patterns in proteins: immunologic kernels.  

PubMed

By applying analysis of the principal components of amino acid physical properties we predicted cathepsin cleavage sites, MHC binding affinity, and probability of B-cell epitope binding of peptides in tetanus toxin and in ten diverse additional proteins. Cross-correlation of these metrics, for peptides of all possible amino acid index positions, each evaluated in the context of a ±25 amino acid flanking region, indicated that there is a strongly repetitive pattern of short peptides of approximately thirty amino acids each bounded by cathepsin cleavage sites and each comprising B-cell linear epitopes, MHC-I and MHC-II binding peptides. Such "immunologic kernel" peptides comprise all signals necessary for adaptive immunologic cognition, response and recall. The patterns described indicate a higher order spatial integration that forms a symbolic logic coordinating the adaptive immune system. PMID:23922927

Bremel, Robert D; Homan, E Jane

2013-01-01

307

Translation-invariant pattern recognition based on Synfire chains  

Microsoft Academic Search

.   Most of current neural network architectures are not suited to recognize a pattern at various displaced positions. This lack\\u000a seems due to the prevailing neuron model which reduces a neuron's information transmission to its firing rate. With this information\\u000a code, a neuronal assembly cannot distinguish between different combinations of its entities and therefore fails to represent\\u000a the fine structure

Hans-Martin R. Arnoldi; Karl-Hans Englmeier; Wilfried Brauer

1999-01-01

308

Spatial-frequency cutoff requirements for pattern recognition in central and peripheral vision  

PubMed Central

It is well known that object recognition requires spatial frequencies exceeding some critical cutoff value. People with central scotomas who rely on peripheral vision have substantial difficulty with reading and face recognition. Deficiencies of pattern recognition in peripheral vision, might result in higher cutoff requirements, and may contribute to the functional problems of people with central-field loss. Here we asked about differences in spatial-cutoff requirements in central and peripheral vision for letter and face recognition. The stimuli were the 26 letters of the English alphabet and 26 celebrity faces. Each image was blurred using a low-pass filter in the spatial frequency domain. Critical cutoffs (defined as the minimum low-pass filter cutoff yielding 80% accuracy) were obtained by measuring recognition accuracy as a function of cutoff (in cycles per object). Our data showed that critical cutoffs increased from central to peripheral vision by 20% for letter recognition and by 50% for face recognition. We asked whether these differences could be accounted for by central/peripheral differences in the contrast sensitivity function (CSF). We addressed this question by implementing an ideal-observer model which incorporates empirical CSF measurements and tested the model on letter and face recognition. The success of the model indicates that central/peripheral differences in the cutoff requirements for letter and face recognition can be accounted for by the information content of the stimulus limited by the shape of the human CSF, combined with a source of internal noise and followed by an optimal decision rule.

Kwon, MiYoung; Legge, Gordon E.

2011-01-01

309

Pattern recognition techniques screening for drugs of abuse with gas chromatography–Fourier transform infrared spectroscopy  

Microsoft Academic Search

As many drugs of abuse are relatively volatile substances, gas chromatography–mass spectrometry (GC–MS), and more recently gas chromatography–Fourier transform infrared spectroscopy (GC–FTIR) became the most powerful techniques applied for their identification. We are presenting a combination of pattern recognition techniques discriminating illicit amphetamines according to the substitution pattern associated with the psychotropic activity (stimulants and hallucinogens) for which they are

M. Praisler; I. Dirinck; J. Van Bocxlaer; A. De Leenheer; D. L. Massart

2000-01-01

310

Determination of neural-network topology for partial discharge pulse pattern recognition  

Microsoft Academic Search

A time-series approach has been employed to devise neural-network topologies for time dependent partial discharge pulse pattern recognition applications. The cascaded output neural-network structure was found to provide the highest success rate in differentiating between two different partial discharge patterns. This was accomplished by utilizing the indexed feature of the first stage output as one of the inputs into the

M. M. A. Salama; R. Bartnikas

2002-01-01

311

PATTERN RECOGNITION/EXPERT SYSTEM FOR MASS SPECTRA OF VOLATILE TOXIC AND OTHER ORGANIC COMPOUNDS  

EPA Science Inventory

A system based on principles of pattern recognition has been developed for identifying toxic and other volatile organic pollutants in complex environmental samples. t interprets the most commonly used monitoring data, mass spectral data, and produces a class designation, an estim...

312

A new eigenstructure method for sinusoidal signal retrieval in white noise: estimation and pattern recognition  

Microsoft Academic Search

A new approach, in a framework of an eigenstructure method using a Hankel matrix, is developed for sinusoidal signal retrieval in white noise. A closed-form solution for the singular pairs of the matrix is defined in terms of the associated sinusoidal signals and noise. The estimated sinusoidal singular vectors are applied to form the noise-free Hankel matrix. A pattern recognition

Baogang Hu; Raymond G. Gosine

1997-01-01

313

A strip chart recorder pattern recognition tool kit for Shuttle operations  

NASA Technical Reports Server (NTRS)

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.

Hammen, David G.; Moebes, Travis A.; Shelton, Robert O.; Savely, Robert T.

1993-01-01

314

Pattern recognition using a family of design algorithms based upon the generalized probabilistic descent method  

Microsoft Academic Search

This paper provides a comprehensive introduction to a novel approach to pattern recognition which is based on the generalized probabilistic descent method (GPD) and its related design algorithms. The paper contains a survey of recent recognizer design techniques, the formulation of GPD, the concept of minimum classification error learning that is closely related to the GPD formalization, a relational analysis

SHIGERU KATAGIRI; Biing-Hwang Juang; Chin-Hui Lee

1998-01-01

315

An improved triangle star pattern recognition algorithm with high identification probability  

Microsoft Academic Search

In this paper, an improved triangle star pattern recognition algorithm is proposed. There are two improvements in the algorithm compared with the classic triangle algorithm. Firstly, the adjacent measured stars in field of view are eliminated and the remaining measured stars are used to build candidate measured star triangles. So high attitude determination precision is obtained if a triangle is

Lei Du; Yan Zhao

2011-01-01

316

How Groups Learn: The Role of Communication Patterns, Cue Recognition, Context Facility, and Cultural Intelligence  

ERIC Educational Resources Information Center

This article explores the role of group learning by focusing on how intragroup communication patterns (implicit and explicit) influence learning readiness dimensions (cue recognition, context facility, and cultural intelligence), which in turn influences the group's ability to learn and the type of leaning that occurs. Groups with high levels of…

Silberstang, Joyce; London, Manuel

2009-01-01

317

Pyrolysis-Mass Spectrometry/Pattern Recognition on a Well-Characterized Suite of Humic Samples.  

National Technical Information Service (NTIS)

A suite of well-characterized humic and fulvic acids of freshwater, soil and plant origin was subjected to pyrolysis-mass spectrometry and the resulting data were analyzed by pattern recognition and factor analysis. A factor analysis plot of the data show...

P. MacCarthy S. J. DeLuca K. J. Voorhees R. L. Malcolm E. M. Thurman

1985-01-01

318

PATTERN RECOGNITION/EXPERT SYSTEM FOR IDENTIFICATION OF TOXIC COMPOUNDS FROM LOW RESOLUTION MASS SPECTRA  

EPA Science Inventory

An empirical rule-based pattern recognition/expert system for classifying, estimating molecular weights and identifying low resolution mass spectra of toxic and other organic compounds has been developed and evaluated. he system was designed to accommodate low concentration spect...

319

Application of pattern recognition techniques to the processing of radar signals  

Microsoft Academic Search

This paper presents numerous pattern recognition techniques which can be applied to radar data. In particular, nearest neighbor and linear discriminant function algorithms are discussed, as well as the use of different sets of features to represent the data. In addition, some of the most common applications are also discussed, including meteorological analyses and military applications.

N. Ezquerra; L. Harkness

1982-01-01

320

Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction  

ERIC Educational Resources Information Center

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…

Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

2012-01-01

321

Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses  

Microsoft Academic Search

We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately

Guanglin Li; Aimee E. Schultz; Todd A. Kuiken

2010-01-01

322

Comparison of SIMCA pattern recognition and library search identification of hazardous compounds from mass spectra  

Microsoft Academic Search

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. A scheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons and polychlorinated biphenyls (PCBs). In addition, partial least squares regression has been used to predict the number of chlorine atoms present in the

M. Sarker; W. G. Glen; L. B. Yin; W. J. Dunn; D. R. Scott

1990-01-01

323

Application of SIMCA (Soft Independent Modeling of Class Analogy) Pattern Recognition to Air Pollutant Analytical Data.  

National Technical Information Service (NTIS)

The SIMCA 3B computer program is a modular, graphics oriented pattern recognition package which can be run on a microcomputer with limited memory, e.g. an Osborne 1 with 64K memory. Principal component analysis is used to classify data with this program. ...

D. R. Scott

1984-01-01

324

Comparison Between Traditional and Pattern Recognition (SIMCA) Strategies in Classification of Old Proteinaceous Binders  

Microsoft Academic Search

An application of multivariate analysis to the characterisation of proteinaceous binders in the field of cultural heritage is provided. We compared identification results of protein binders obtained with three classical strategies and a more robust pattern recognition technique as SIMCA. A collection of natural binders prepared according to old recipes was used as reference material (training set) to develop SIMCA

R. Checa; E. Manzano; L. R. Rodríguez-Simón; L. F. Capitan-Vallvey

325

Pattern recognition of partial discharge in XLPE cables using a neural network  

Microsoft Academic Search

An experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system is described. The NN system was a three-layer artificial neural network system with feedforward connections, and its learning method was a backpropagation algorithm incorporating an external teacher signal. Input information for the NN was a combination of

H. Suzuki; T. Endoh

1992-01-01

326

Classification of Brazilian vinegars according to their 1H NMR spectra by pattern recognition analysis  

Microsoft Academic Search

This work describes using 1H NMR data and pattern recognition analysis to classify vinegars. Vinegar authenticity is linked to raw ingredient source and manufacturing conditions. Application of PCA and HCA methods resulted in the natural clustering of the samples according to the raw material used. Wine vinegars were characterized by a high concentration of ethyl acetate, glycerol, methanol and tartaric

Elisangela F. Boffo; Leila A. Tavares; Márcia M. C. Ferreira; Antonio G. Ferreira

2009-01-01

327

Incremental 2-directional 2-dimensional linear discriminant analysis for multitask pattern recognition  

Microsoft Academic Search

In this paper, we propose an incremental 2- directional 2-dimensional linear discriminant analysis (I- (2D) 2 LDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. In I-(2D) 2 LDA, a discriminant space of the current task

Chunyu Liu; Young-Min Jang; Seiichi Ozawa; Minho Lee

2011-01-01

328

Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems  

Microsoft Academic Search

In this paper, we propose a new incremental linear discriminant analysis (ILDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. The Pang et al.'s ILDA is extended such that a discriminant space of the current task

Masayuki Hisada; Seiichi Ozawa; Kau Zhang; Nikola Kasabov

2010-01-01

329

Supervised pattern recognition to discriminate the geographical origin of rice bran oils: a first study  

Microsoft Academic Search

Supervised pattern recognition appears to be a useful tool to authenticate foodstuffs according to their geographical or varietal origin, when a set of samples whose classification is known a priori are available. In this work, linear discriminant analysis and artificial neural networks trained by the back-propagation algorithm have been used to discriminate rice bran oils manufactured in three different countries

Federico Marini; Fabrizio Balestrieri; Remo Bucci; Domenico Marini

2003-01-01

330

Integrating MPEG7 Descriptors and Pattern Recognition: An Environment for Multimedia Indexing and Searching  

Microsoft Academic Search

This paper describes a music searching system based on an automatic indexing process created over a pattern recognition tool and MPEG-7 standard. We present a complete analysis of the involved technologies integration process and show a reference measure in the feasibility of this kind of application not only in the research environment but also in the production of real systems.

Reinaldo Matushima; Daniel Makoto Hiramatsu; Regina Melo Silveira; Wilson Vicente Ruggiero; Carlos Eduardo Machado Da Costa; Mauricio Mario Monteiro; Celso Hatori

2004-01-01

331

Dual-band, infrared buried mine detection using a statistical pattern recognition approach  

SciTech Connect

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.

Buhl, M.R.; Hernandez, J.E.; Clark, G.A.; Sengupta, S.K.

1993-08-01

332

The design of rotation-invariant pattern recognition using the silicon retina  

Microsoft Academic Search

A new rotation-invariant pattern recognition system is proposed and analyzed. In this system, silicon retina cells capable of image sensing and edge extraction are used so that the system can directly process images from the real world without an extra edge detector. The rotation-invariant discrete correlation function is modified and implemented in the silicon retina structure by using the current

Chin-Fong Chiu; Chung-Yu Wu

1997-01-01

333

Pattern recognition-a technique for induction machines rotor broken bar detection  

Microsoft Academic Search

A pattern recognition technique based on Bayes minimum error classifier is developed to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. Initially, rotor speed is estimated from the stator currents, then appropriate features are extracted. The produced feature vector is

Masoud Haji; Hamid A. Toliyat

2001-01-01

334

Beyond pattern recognition: five immune checkpoints for scaling the microbial threat  

Microsoft Academic Search

Pattern recognition by the innate immune system enables the detection of microorganisms, but how the level of microbial threat is evaluated — a process that is crucial for eliciting measured antimicrobial responses with minimal inflammatory tissue damage — is less well understood. New evidence has shown that features of microbial viability can be detected by the immune system and thereby

J. Magarian Blander; Leif E. Sander

2012-01-01

335

Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques  

Microsoft Academic Search

Active volcanoes generate sonic and infrasonic signals, whose investigation provides useful information for both monitoring purposes and the study of the dynamics of explosive phenomena. At Mt. Etna volcano (Italy), a pattern recognition system based on infrasonic waveform features has been developed. First, by a parametric power spectrum method, the features describing and characterizing the infrasound events were extracted: peak

A. Cannata; P. Montalto; M. Aliotta; C. Cassisi; A. Pulvirenti; E. Privitera; D. Patanè

2011-01-01

336

Dynamic Programming as Applied to Feature Subset Selection in a Pattern Recognition System  

Microsoft Academic Search

This paper proposes dynamic programming search procedures to expedite the feature subset selection processes in a pattern recognition system. It is shown that in general the proposed procedures require much fewer subsets to be evaluated than the exhaustive search procedure. For example, a problem of selecting the best subset of 4 features from a set of 24 features requires an

Chieng-Yi Chang

1973-01-01

337

Dynamic programming as applied to feature subset selection in a pattern recognition system  

Microsoft Academic Search

This paper proposes dynamic programming search procedures to expedite the feature subset selection processes in a pattern recognition system. It is shown that in general the proposed procedures require far less number of subsets to be evaluated than the exhaustive search procedure. For example, a problem of selecting the best subset of 4 features from a set of 24 features

C. Y. Chang

1972-01-01

338

CATEGORISATION AND PATTERN RECOGNITION METHODS FOR DAMAGE LOCALISATION FROM VIBRATION MEASUREMENTS  

Microsoft Academic Search

This study presents a categorisation (classification) approach towards the damage localisation problem from vibration measurements. A stochastic pattern recognition method for solving such problems is introduced. The method suggests the substructuring in order to reduce the possible damage locations to the number of substructures. It utilises the differences in the frequency response functions of the structure in the damaged and

I. Trendafilova; W. Heylen

2003-01-01

339

PYROLYSIS-MASS SPECTROMETRY/PATTERN RECOGNITION ON A WELL-CHARACTERIZED SUITE OF HUMIC SAMPLES  

EPA Science Inventory

A suite of well-characterized humic and fulvic acids of freshwater, soil and plant origin was subjected to pyrolysis-mass spectrometry and the resulting data were analyzed by pattern recognition and factor analysis. A factor analysis plot of the data shows that the humic acids an...

340

Pattern-Recognition Methods Applied to the Problem of Noise-Proof Decoding.  

National Technical Information Service (NTIS)

The problem of noise-proof decoding is considered as a particular case of pattern recognition. The use of a perceptron as a decoder in a binary symmetric channel is substantiated. An algorithm is developed which generalizes the nearby elements of the inpu...

V. A. Andreev V. V. Mikhailov D. V. Usanova

1969-01-01

341

Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques  

Microsoft Academic Search

An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and

R. M. Singer; K. C. Gross; K. E. Humenik

1991-01-01

342

LARGE SCALE EVALUATION OF A PATTERN RECOGNITION/EXPERT SYSTEM FOR MASS SPECTRAL MOLECULAR WEIGHT ESTIMATION  

EPA Science Inventory

A fast, personal-computer based method of estimating molecular weights of organic compounds from low resolution mass I spectra has been thoroughly evaluated. he method is based on a rule-based pattern,recognition/expert system approach which uses empirical linear corrections whic...

343

Three dimensional pattern recognition using feature-based indexing and rule-based search  

NASA Astrophysics Data System (ADS)

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. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database. Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR).

Lee, Jae-Kyu

344

A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration after Incomplete Cervical Spinal Cord Injury  

PubMed Central

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.

Liu, Jie; Zhou, Ping

2013-01-01

345

Real-time holographic pattern recognition with bacteriorhodopsin films  

NASA Astrophysics Data System (ADS)

The biological photochrome bacteriorhodopsin (BR) has attractive photophysical properties which allow its use as the photoactive component for dynamic recording media for optical applications. Purple membrane (PM) patches, which contain BR in a two-dimensional crystalline lattice, are isolated from Halobacterium halobium. Polymeric films with embedded PM are well suited reversible media for holographic recording. In addition, artificial derivatives of BR with improved optical properties can be generated by genetic methods and isolated from the mutated halobacterial strains. The high reversibility (> 106 record/erase cycles), the fast timescale of its photoconversions (fs - ms), and the high resolution (> 5000 lines/mm) make these films suitable media for real-time holographic applications. A dual-axis joint-Fourier-transform correlator is described with two liquid crystal television screens as input devices and a BR-film as active holographic material in the Fourier plane. The experimental data presented demonstrate that this system is capable of processing two independent video signals in real-time with a signal-to-noise ratio of 45 dB. The polarization recording properties of BR-films offer an efficient method to separate the correlation signal from scattered light.

Hampp, Norbert; Thoma, Ralph; Braeuchle, Christoph R.; Oesterhelt, Dieter

1993-03-01

346

Polynomial distance classifier correlation filter for pattern recognition  

NASA Astrophysics Data System (ADS)

We introduce what is to our knowledge a new nonlinear shift-invariant classifier called the polynomial distance classifier correlation filter (PDCCF). The underlying theory extends the original linear distance classifier correlation filter [Appl. Opt. 35, 3127 (1996)] to include nonlinear functions of the input pattern. This new filter provides a framework (for combining different classification filters) that takes advantage of the individual filter strengths. In this new filter design, all filters are optimized jointly. We demonstrate the advantage of the new PDCCF method using simulated and real multi-class synthetic aperture radar images.

Alkanhal, Mohamed; Vijaya Kumar, B. V. K.

2003-08-01

347

The spatial vision tree: a generic pattern recognition engine: scientific foundations, design principles, and preliminary tree design  

Microsoft Academic Search

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function

Zia-Ur Rahman; Daniel J. Jobson; Glenn A. Woodell

2010-01-01

348

Critical song features for auditory pattern recognition in crickets.  

PubMed

Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females' phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. PMID:23437054

Meckenhäuser, Gundula; Hennig, R Matthias; Nawrot, Martin P

2013-01-01

349

Emotional faces in context: age differences in recognition accuracy and scanning patterns.  

PubMed

Although age-related declines in facial expression recognition are well documented, previous research has relied mostly on isolated faces devoid of context. The authors 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 the highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were the lowest. 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

Noh, Soo Rim; Isaacowitz, Derek M

2013-04-01

350

An associative memory readout for ESNs with applications to dynamical pattern recognition.  

PubMed

The use of echo state networks (ESN) to find patterns in time (dynamical pattern recognition) has been limited. This paper argues that ESNs are particularly well suited for dynamical pattern recognition and proposes a linear associative memory (LAM) as a novel readout for ESNs. From the class of LAMs, the minimum average correlation energy (MACE) filter is adopted because of its high rejection characteristics that allow its use as a detector in the automatic pattern recognition literature. In the ESN application, the MACE interprets the states of the ESN as a two-dimensional "image", one dimension being time and the other the processing element index (space). An optimal template image for each class, which associates ESN states with the class label, can be analytically computed using training data. During testing, ESN states are correlated with each template image and the class label of the template with the highest correlation is assigned to the input pattern. The ESN-MACE combination leads to a nonlinear template matcher with robust noise performance as needed in non-Gaussian, nonlinear digital communication channels. A real-world data experiment for chemical sensing with an electronic nose is included to demonstrate the validity of this approach. Moreover, the proposed readout can also be used with liquid state machines eliminating the need to convert spike trains into continuous signals by binning or low-pass filtering. PMID:17513087

Ozturk, Mustafa C; Principe, José C

2007-04-01

351

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

PubMed

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates. PMID:21215868

Lavine, Barry K; Mirjankar, Nikhil; Vander Meer, Robert K

2011-01-30

352

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

PubMed

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates. PMID:21035667

Lavine, Barry K; Mirjankar, Nikhil; Vander Meer, Robert K

2010-11-15

353

Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms.  

PubMed

EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control. PMID:24111088

Zhang, Xiaorong; Huang, He; Yang, Qing

2013-01-01

354

Local directional pattern of phase congruency features for illumination invariant face recognition  

NASA Astrophysics Data System (ADS)

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.

Essa, Almabrok E.; Asari, Vijayan K.

2014-04-01

355

Adsorption and pattern recognition of polymers at complex surfaces with attractive stripelike motifs.  

PubMed

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. PMID:24766025

Möddel, Monika; Janke, Wolfhard; Bachmann, Michael

2014-04-11

356

Retinotopically specific reorganization of visual cortex for tactile pattern recognition  

PubMed Central

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.

Cheung, Sing-Hang; Fang, Fang; He, Sheng; Legge, Gordon E.

2009-01-01

357

Aircraft Rivets Defect Recognition Method Based on Magneto-optical Images  

Microsoft Academic Search

With magneto-optical images for the aircraft rivets, a new automated recognition algorithm of inspecting the existence and the direction of cracks based on fuzzy support vector machine (FSVM) is presented. The binary image of rivet is obtained by preprocessing the magneto-optic image, the star radial vector method is used to acquire the feature of rivet edge, and the approximate center

Bo Li; Xiangfeng Wang; Hongping Yang; Zhenliu Zhou

2010-01-01

358

[Application of two FTIR pattern recognition methods to the Zanthoxylum nitidum geographical origins determination].  

PubMed

In the present work, the authors explored a rapid method of the Zanthoxylum nitidum geographical origins determination. Based on Fourier transform infrared spectroscopy (FTIR) technology, the band of 1 800-400 cm(-1) which is the IR fingerprint of Zanthoxylum nitidum, the Fisher ratio and the soft independent modeling of class analogies (SIMCA) were used to build a classification model. Respectively, four kinds of Zanthoxylum nitidum in the Guangxi region were detected by the model, and the model was verified by calculating their recognition rate and rejection rate. The results show that the authors can accurately extract the overall information of Chinese herbal medicines by using the FTIR, also established a pattern recognition model to predict unknown samples, and obtained satisfactory recognition rate and rejection rate, indicating that the model has stronger ability of identification. The detection on real time was carried out rapidly with the Fisher model, suggesting that the model has more practical value. PMID:22250538

Mao, Xiao-Li; Zheng, Juan-Mei; Li, Zi-Da; Lei, Xin-Chao; Huang, Shu-Shi; Liu, Hua-Gang

2011-10-01

359

Real-time optical multiple object recognition and tracking system and method  

NASA Technical Reports Server (NTRS)

System for optically recognizing and tracking a plurality of objects within a field of vision. Laser (46) produces a coherent beam (48). Beam splitter (24) splits the beam into object (26) and reference (28) beams. Beam expanders (50) and collimators (52) transform the beams (26, 28) into coherent collimated light beams (26', 28'). A two-dimensional SLM (54), disposed in the object beam (26'), modulates the object beam with optical information as a function of signals from a first camera (16) which develops X and Y signals reflecting the contents of its field of vision. A hololens (38), positioned in the object beam (26') subsequent to the modulator (54), focuses the object beam at a plurality of focal points (42). A planar transparency-forming film (32), disposed with the focal points on an exposable surface, forms a multiple position interference filter (62) upon exposure of the surface and development processing of the film (32). A reflector (53) directing the reference beam (28') onto the film (32), exposes the surface, with images focused by the hololens (38), to form interference patterns on the surface. There is apparatus (16', 64) for sensing and indicating light passage through respective ones of the positions of the filter (62), whereby recognition of objects corresponding to respective ones of the positions of the filter (62) is affected. For tracking, apparatus (64) focuses light passing through the filter (62) onto a matrix of CCD's in a second camera (16') to form a two-dimensional display of the recognized objects.

Chao, Tien-Hsin (Inventor); Liu, Hua-Kuang (Inventor)

1990-01-01

360

Host-Viral Interactions: Role of Pattern Recognition Receptors (PRRs) in Human Pneumovirus Infections  

PubMed Central

Acute respiratory tract infection (RTI) is a leading cause of morbidity and mortality worldwide and the majority of RTIs are caused by viruses, among which respiratory syncytial virus (RSV) and the closely related human metapneumovirus (hMPV) figure prominently. Host innate immune response has been implicated in recognition, protection and immune pathological mechanisms. Host-viral interactions are generally initiated via host recognition of pathogen-associated molecular patterns (PAMPs) of the virus. This recognition occurs through host pattern recognition receptors (PRRs) which are expressed on innate immune cells such as epithelial cells, dendritic cells, macrophages and neutrophils. Multiple PRR families, including Toll-like receptors (TLRs), RIG-I-like receptors (RLRs) and NOD-like receptors (NLRs), contribute significantly to viral detection, leading to induction of cytokines, chemokines and type I interferons (IFNs), which subsequently facilitate the eradication of the virus. This review focuses on the current literature on RSV and hMPV infection and the role of PRRs in establishing/mediating the infection in both in vitro and in vivo models. A better understanding of the complex interplay between these two viruses and host PRRs might lead to efficient prophylactic and therapeutic treatments, as well as the development of adequate vaccines.

Kolli, Deepthi; Velayutham, Thangam Sudha; Casola, Antonella

2013-01-01

361

Pattern-recognition software detecting the onset of failures in complex systems  

SciTech Connect

A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself.

Mott, J.; King, R.

1987-01-01

362

Several practical issues toward implementing myoelectric pattern recognition for stroke rehabilitation.  

PubMed

High density surface electromyogram (sEMG) recording and pattern recognition techniques have demonstrated that substantial motor control information can be extracted from neurologically impaired muscles. In this study, a series of pattern recognition parameters were investigated in classification of 20 different movements involving the affected limb of 12 chronic stroke subjects. The experimental results showed that classification performance could be improved with spatial filtering and be maintained with a limited number of electrodes. It was also found that appropriate adjustment of analysis window length, sampling rate, and high-pass cut-off frequency in sEMG conditioning and processing would be potentially useful in reducing computational cost and meanwhile ensuring classification performance. The quantitative analyses are useful for practical myoelectric control toward improved stroke rehabilitation. PMID:24525007

Li, Yun; Chen, Xiang; Zhang, Xu; Zhou, Ping

2014-06-01

363

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations  

NASA Astrophysics Data System (ADS)

We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M9) on M(111), where M = Cu, Ag or Ni.

Islamuddin Shah, Syed; Nandipati, Giridhar; Kara, Abdelkader; Rahman, Talat S.

2012-09-01

364

Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control.  

PubMed

Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This "tunes" the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly ( p < 0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects. PMID:19473932

Hargrove, Levi J; Li, Guanglin; Englehart, Kevin B; Hudgins, Bernard S

2009-05-01

365

Comparing Shape and Texture Features for Pattern Recognition in Simulation Data  

SciTech Connect

Shape and texture features have been used for some time for pattern recognition in datasets such as remote sensed imagery, medical imagery, photographs, etc. In this paper, we investigate shape and texture features for pattern recognition in simulation data. In particular, we explore which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations. Three texture features--gray level co-occurrence matrices, wavelets, and Gabor filters--and two shape features--geometric moments and the angular radial transform--are compared. The features are evaluated using a similarity retrieval framework. Our preliminary results indicate that Gabor filters perform the best among the texture features and the angular radial transform performs the best among the shape features. The feature which performs the best overall is dependent on how the groundtruth dataset is created.

Newsam, S; Kamath, C

2004-12-10

366

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques  

NASA Technical Reports Server (NTRS)

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

Melhorn, W. N.; Sinnock, S.

1973-01-01

367

Three-Dimensional Color Pattern Recognition Using Fringe-Adjusted Joint Transform Correlation With CIELab Coordinates  

Microsoft Academic Search

A new 3-D color pattern recognition technique, utilizing the concept of a fringe-adjusted joint transform correlator (JTC) and CIELab color space, is proposed in this paper. The proposed technique yields better discrimination capability and sharper and stronger correlation peak intensity as compared with a classical JTC with conventional red-green-blue (RGB) components. Simulation results are presented to verify the robustness of

Mohammad S. Alam; Sheue Feng Goh; Srikanth Dacharaju

2010-01-01

368

Reading the viral signature by Toll-like receptors and other pattern recognition receptors  

Microsoft Academic Search

Successful host defense against viral infections relies on early production of type I interferon (IFN) and subsequent activation of a cellular cytotoxic response. The acute IFN and inflammatory response against virus infections is mediated by cellular pattern-recognition receptors (PRRs) that recognize specific molecular structures on viral particles or products of viral replication. Toll-like receptors (TLRs) constitute a class of membrane-bound

Trine H. Mogensen; Søren R. Paludan

2005-01-01

369

Applications of matrix derivatives to optimization problems in statistical pattern recognition  

NASA Technical Reports Server (NTRS)

A necessary condition for a real valued Frechet differentiable function of a vector variable have an extremum at a vector x sub 0 is that the Frechet derivative vanishes at x sub 0. A relationship between Frechet differentials and matrix derivatives was established that obtains a necessary condition on the matrix derivative at an extrema. These results are applied to various scalar functions of matrix variables which occur in statistical pattern recognition.

Morrell, J. S.

1975-01-01

370

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies  

Microsoft Academic Search

ASTM D6729 gas chromatographic fingerprinting coupled to pattern-recognition multivariate soft independent modeling of class\\u000a analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline\\u000a quality. SIMCA, was performed on gas chromatographic fingerprints to classify the quality of representative commercial gasoline\\u000a samples selected by hierarchical cluster analysis and collected over a 5 month period from gas stations

Rafael Rodrigues Hatanaka; Danilo Luiz Flumignan; José Eduardo de Oliveira

2009-01-01

371

AUTHENTICATION OF FUEL SPILL STANDARDS USING GAS CHROMATOGRAPHY\\/PATTERN RECOGNITION TECHNIQUES  

Microsoft Academic Search

Gas chromatography and pattern recognition techniques have been used to develop a potential method to authenticate fuel spill identification standards. The test data consisted of 65 gas chromatograms of JP-4, Jet-A, JP-7, JPTS, JP-5, AVGAS, and Diesel fuels, which were obtained from Mukilteo and Wright Patterson Energy Management Laboratories. A genetic algorithm (GA) was used to identify features in the

B. K. Lavine; Anja Vesanen; D. M. Brzozowski; H. T. Mayfield

2001-01-01

372

Maximally Informative Feature and Sensor Selection in Pattern Recognition Using Local and Global Independent Component Analysis  

Microsoft Academic Search

In pattern recognition, a suitable criterion for feature selection is the mutual information (MI) between feature vectors\\u000a and class labels. Estimating MI in high dimensional feature spaces is problematic in terms of computation load and accuracy.\\u000a We propose an independent component analysis based MI estimation (ICA-MI) methodology for feature selection. This simplifies\\u000a the high dimensional MI estimation problem into multiple

Tian Lan; Deniz Erdogmus

2007-01-01

373

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features  

Microsoft Academic Search

For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats

Wolfgang Kabsch; Christian Sander

1983-01-01

374

The estimation of the gradient of a density function, with applications in pattern recognition  

Microsoft Academic Search

Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistency of the estimates. The results are generalized to obtain a simple mcan-shift estimate that can be extended in ak-nearest-neighbor approach. Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality

KEINOSUKE FUKUNAGA; LARRY D. HOSTETLER

1975-01-01

375

Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition  

Microsoft Academic Search

The task of locating buried utilities using ground penetrating radar is addressed, and a novel processing technique computationally suitable for on-site imaging is proposed. The developed system comprises a neural network classifier, a pattern recognition stage, and additional pre-processing, feature-extraction and image processing stages. Automatic selection of the areas of the radargram containing useful information results in a reduced data

W. Alnuaimy; Y. Huang; M. Nakhkash; M. T. C Fang; V. T Nguyen; A. Eriksen

2000-01-01

376

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network  

Microsoft Academic Search

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform

Zhongqing Su; Lin Ye

2004-01-01

377

Development and evaluation of pattern recognition techniques for fluorescence diagnosis of atherosclerosis  

NASA Astrophysics Data System (ADS)

A field where fluorescence spectroscopy might be of great interest for diagnosis, is coronary atherosclerosis and therefore spectroscopic characterization of cardiovascular tissues has been extensively studied. Nevertheless there are several limitations in the precise interpretation of the spectroscopic differences, between normal and atherosclerotic arteries since the tissue is a complex and multilayer structure. Therefore the spectra of individual chromophores could overlap and re-absorption phenomena could occur, too. Another major difficulty arises from the necessity of convenient classification algorithms and the assessment of their feasibility to use fluorescence information, for accurate diagnosis. As a result in order to assess the feasibility of utilizing spectral information to discriminate arterial tissue type several classification algorithms were developed and evaluated. In this work the following pattern recognition techniques have been tested and evaluated: (1) Distance measure (or norm, or metric) based pattern recognition techniques. Methodologically speaking, based on the histopathological diagnosis, a training set of spectra has been classified into four different categories (healthy, fibrous, calcified, heavy calcified) and in each of these four training groups a representative spectrum has been recorded. (2) A pattern recognition method based on statistical considerations. Discrimination between either the four aforementioned classes (categories) or pairs of them is achieved since peak intensities in appropriate wavelengths appear to correlate efficiently with tissue type. The difference of each training set member from the corresponding representative has been defined by using various appropriate distance measures and the sample statistical properties for each category of the training group has been found. Appropriate statistical analysis has been performed in order to deduce the distribution of the distance measures and of the coefficients of the whole population for each one of the four categories, with at least 99% confidence interval. A validation set of samples has been used in order to test and compare the aforementioned pattern recognition algorithms. A performance comparison of the aforementioned algorithms has been undertaken.

Papaodysseus, Constantinos N.; Kassis, Kyriakos A.; Gonis, Helen; Yova, Dido

1996-05-01

378

Optical Data Processing.  

National Technical Information Service (NTIS)

Our research concerns optical data processing for missile guidance and target recognition. It uses pattern recognition techniques with an increasing use of knowledge base, inference machine and associative processor techniques. Our Year 2 work addresses d...

D. Casasent

1986-01-01

379

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study  

PubMed Central

Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and abnormal muscle patterns and provide feedback on their correct recruitment.

2013-01-01

380

Air Force research in optical processing  

NASA Astrophysics Data System (ADS)

Optical and optical electronic hybrid processing especially in the application area of image processing are emphasized. Real time pattern recognition processors for such airborne missions as target recognition, tracking, and terminal guidance are studied.

Neff, J.

1981-12-01

381

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques  

PubMed Central

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

Lee, Sean; Nitin, Mantri

2012-01-01

382

A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control.  

PubMed

Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of approximately 95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited movement speed when there was a change in classifier decision. The goal of this velocity ramp was to improve prosthesis positioning by minimizing the effect of unintended movements. Two patients who had undergone TMR surgery controlled either a virtual or physical prosthesis. They completed a Target Achievement Control Test where they commanded a virtual prosthesis into a target posture. Participants showed improved performance metrics of 34% increase in completion rate and 13% faster overall time with the velocity ramp compared to without the velocity ramp. One participant controlled a physical prosthesis and in three minutes was able to create a tower of 1" cubes seven blocks tall with the velocity ramp compared to a tower of only two blocks tall in the control condition. These results suggest that using a pattern recognition system with a decision based velocity profile may improve user performance. PMID:19964513

Simon, Ann M; Hargrove, Levi J; Lock, Blair A; Kuiken, Todd A

2009-01-01

383

Teaching image processing and pattern recognition with the Intel OpenCV library  

NASA Astrophysics Data System (ADS)

In this paper we present an approach to teaching image processing and pattern recognition with the use of the OpenCV library. Image processing, pattern recognition and computer vision are important branches of science and apply to tasks ranging from critical, involving medical diagnostics, to everyday tasks including art and entertainment purposes. It is therefore crucial to provide students of image processing and pattern recognition with the most up-to-date solutions available. In the Institute of Electronics at the Technical University of Lodz we facilitate the teaching process in this subject with the OpenCV library, which is an open-source set of classes, functions and procedures that can be used in programming efficient and innovative algorithms for various purposes. The topics of student projects completed with the help of the OpenCV library range from automatic correction of image quality parameters or creation of panoramic images from video to pedestrian tracking in surveillance camera video sequences or head-movement-based mouse cursor control for the motorically impaired.

Koz?owski, Adam; Królak, Aleksandra

2009-06-01

384

Content-addressable holographic data storage system for invariant pattern recognition of gray-scale images.  

PubMed

Conventionally a holographic data storage system uses binary digital data as the input pages. We propose and demonstrate the use of a holographic data storage system for the purpose of invariant pattern recognition of gray-scale images. To improve the correlation accuracy for gray-scale images, we present a coding technique, phase Fourier transform (phase-FT) coding, to code a gray-scale image into a random and balanced digital binary image. In addition to the fact that a digital data page is obtained for incorporation into a holographic data storage system, this phase-FT coded image produces dc-free homogenized Fourier spectrum. This coded image can also be treated as an image for further processing, such as synthesis of distortion-invariant filters for invariant pattern recognition. A space-domain synthetic discriminant function (SDF) filter has been synthesized using these phase-FT coded images for rotation-invariant pattern recognition. Both simulation and experimental results are presented. The results show good correlation accuracy in comparison to correlation results obtained for SDF filter synthesized using the original gray-scale images themselves. PMID:20090813

Joseph, Joby; Bhagatji, Alpana; Singh, Kehar

2010-01-20

385

Developement of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)  

SciTech Connect

Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have 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 processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. 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 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. Significant improvement in the architecture of associative memory structures is needed to run fast pattern recognition algorithms of this scale. We are proposing the development of 3D integrated circuit technology as a way to implement new associative memory structures for fast pattern recognition applications. Adding a 'third' dimension to the signal processing chain, as compared to the two-dimensional nature of printed circuit boards, Field Programmable Gate Arrays (FPGAs), etc., opens up the possibility for new architectures that could dramatically enhance pattern recognition capability. We are currently performing preliminary design work to demonstrate the feasibility of this approach. In this proposal, we seek to develop the design and perform the ASIC engineering necessary to realize a prototype device. While our focus here is on the Energy Frontier (e.g. the LHC), the approach may have applications in experiments in the Intensity Frontier and the Cosmic Frontier as well as other scientific and medical projects. In fact, the technique that we are proposing is very generic and could have wide applications far beyond track trigger, both within and outside HEP.

Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; /Fermilab; Shochet, M.; Tang, F.; /Chicago U.; Demarteau, M.; /Argonne /INFN, Padova

2011-04-13

386

Pattern Recognition  

NSDL National Science Digital Library

CSC 475. Topics in Computer Science (3) Prerequisite: Senior standing and permission of instructor. Topics of current interest in computer science not covered in existing courses. May be repeated under a different subtitle

Tagliarini, Gene

2005-04-20

387

Search and retrieval of plasma wave forms: Structural pattern recognition approach  

NASA Astrophysics Data System (ADS)

Databases for fusion experiments are designed to store several million wave forms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques can allow identification of similar plasma behaviors. Further developments in this area must be focused on four aspects: large databases, feature extraction, similarity function, and search/retrieval efficiency. This article describes an approach for pattern searching within wave forms. The technique is performed in three stages. Firstly, the signals are filtered. Secondly, signals are encoded according to a discrete set of values (code alphabet). Finally, pattern recognition is carried out via string comparisons. The definition of code alphabets enables the description of wave forms as strings, instead of representing the signals in terms of multidimensional data vectors. An alphabet of just five letters can be enough to describe any signal. In this way, signals can be stored as a sequence of characters in a relational database, thereby allowing the use of powerful structured query languages to search for patterns and also ensuring quick data access.

Dormido-Canto, S.; Farias, G.; Vega, J.; Dormido, R.; Sánchez, J.; Duro, N.; Santos, M.; Martin, J. A.; Pajares, G.

2006-10-01

388

Versatile functional microstructured polystyrene-based platforms for protein patterning and recognition.  

PubMed

We report the preparation of different functional surface patterns based on the optimization of the photo-cross-linking/degradation kinetics of polystyrene (PS) upon exposure to UV-light. We employed a PS-b-PGA (polystyrene-block-poly(l-glutamic acid)) block copolymer that will, in addition to the surface pattern, provide functionality. By using short irradiation times, PS can be initially cross-linked, whereas an excess of the exposure time provokes the degradation of the material. As a result of the optimization of time of exposure, the use of an appropriate cover, or the incorporation of an appropriate amount of absorbing active species (photoinitiator), different tailor-made surface patterns can be obtained, from boxes to needles. Moreover, in addition to the surface pattern, we introduced changes on the chemical composition of the polystyrene using an amphiphilic block copolymer (for instance, we employ PS-b-PGA) that will provide functional surfaces with major advantages. In particular, the presence of carboxylic functional groups provides a unique opportunity to anchor, for instance polypeptide sequences. We describe the immobilization of polypeptide sequences in precise surface positions that allows the use of the surfaces for protein recognition purposes. The immobilization of the proteins evidence the success of the recognition and opens a new alternative for protein patterning on surfaces for many biotechnological and biomedical applications. PMID:23901941

Palacios-Cuesta, Marta; Cortajarena, Aitziber L; García, Olga; Rodríguez-Hernández, Juan

2013-09-01

389

Hand biometric recognition based on fused hand geometry and vascular patterns.  

PubMed

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%. PMID:23449119

Park, GiTae; Kim, Soowon

2013-01-01

390

Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns  

PubMed Central

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%.

Park, GiTae; Kim, Soowon

2013-01-01

391

The Relationship between Word and Stress Pattern Recognition Ability and Hearing Level in Hearing-Impaired Young Adults.  

ERIC Educational Resources Information Center

The relationship between word and stress pattern recognition ability and hearing level was explored by administering the Children's Auditory Test to hearing-impaired young adults (N=27). For word recognition, subjects with average hearing loss between 85 and 100 decibels demonstrated a wide range of performance not predictable from their…

Jackson, Pamela; Kelly-Ballweber, Denise

1986-01-01

392

Research on rapid detection of total bacteria in juice based on biomimetic pattern recognition and machine vision  

Microsoft Academic Search

In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice, biomimetic pattern recognition and machine vision were employed. The characteristic data, such as shape, texture and color features, were acquired by using the machine vision technology from bacteria images in varieties of juice. Based on multi-weight higher order neuron network, the recognition models

Shenglang Jin; Yongguang Yin

2010-01-01

393

Patterns of Word Recognition Errors Among Adult Basic Education Native and Nonnative Speakers of English. NCSALL Research Brief.  

ERIC Educational Resources Information Center

The patterns of word recognition errors among native and nonnative speakers of English in adult basic education classes were compared in a study that focused on the 212 of the 676 learners in the Adult Reading Components Study who scored between grade equivalent (GE) 4 and 6 in word recognition. Key findings were as follows: (1) highly similar…

Davidson, Rosalind Kasle; Strucker, John

394

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine  

NASA Astrophysics Data System (ADS)

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

395

Preprocessing, Variable Selection, and Classification Rules in the Application of SIMCA Pattern Recognition to Mass Spectral Data.  

National Technical Information Service (NTIS)

In a recent report a strategy was proposed for the classification and identification of toxic organic compounds observed in ambient air from mass spectra using computational pattern recognition based on SIMCA principal components modeling of the autocorre...

W. J. Dunn S. L. Emery W. Graham Glen D. R. Scott

1989-01-01

396

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms  

PubMed Central

Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.

Sensinger, Jonathon W.; Lock, Blair A.; Kuiken, Todd A.

2011-01-01

397

Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis  

Microsoft Academic Search

Two-dimensional comprehensive gas chromatography (GC×GC) is applied to a pattern recognition problem involving classification of jet fuel mixtures. Analysis of variance (ANOVA)-based feature selection is initially used to identify and select chromatographic features relevant to a given classification in two studies. Then, principal component analysis (PCA) was used for pattern recognition classification. In the first study, a 1% volumetric composition

Kevin J Johnson; Robert E Synovec

2002-01-01

398

Study of stability of time-domain features for electromyographic pattern recognition  

PubMed Central

Background Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition. Methods Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study. Results Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features. Conclusions Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.

2010-01-01

399

[A leukocyte pattern recognition based on feature fusion in multi-color space].  

PubMed

To solve the ineffective problem of leukocytes classification based on multi-feature fusion in a single color space, we proposed an automatic leukocyte pattern recognition by means of feature fusion with color histogram and texture granular in multi-color space. The interactive performance of three color spaces (RGB, HSV and Lab), two features (color histogram and texture granular) and four similarity measured distance metrics (normalized intersection, Euclidean distance, chi2-metric distance and Mahalanobis distance) were discussed. The optimized classification modes of high precision, extensive universality and low cost to different leukocyte types were obtained respectively, and then the recognition system of tree-integration of the optimized modes was established. The experimental results proved that the performance of the fusion classification was improved by 12.3% at least. PMID:24459942

Hao, Liangwang; Hong, Wenxue

2013-10-01

400

Comparative study of the CCF-like pattern recognition protein in different Lumbricid species.  

PubMed

Coelomic fluid of the Lumbricid Eisenia fetida contains a 42-kDa pattern recognition protein named coelomic cytolytic factor (CCF) that binds microbial cell wall components and triggers the activation of the prophenoloxidase cascade, an important invertebrate defense pathway. Here we report on the sequence characterization of CCF-like molecules of other Lumbricids: Aporrectodea caliginosa, Aporrectodea icterica, Aporrectodea longa, Aporrectodea rosea, Dendrobaena veneta, Lumbricus rubellus and Lumbricus terrestris, and show that CCF from E. fetida has a broader saccharide-binding specificity, being the only one recognizing N,N'-diacetylchitobiose. We suggest that the broad recognition repertoire of E. fetida CCF reflects a particular microbial environment this species lives in. PMID:16386303

Silerová, Marcela; Procházková, Petra; Josková, Radka; Josens, Guy; Beschin, Alain; De Baetselier, Patrick; Bilej, Martin

2006-01-01

401

Determination of temperature and pressure patterns associated to sea ice fields in Antarctica, with modern tools of pattern recognition  

NASA Astrophysics Data System (ADS)

Monthly sea ice anomalies derived from passive microwave satellite data for Antarctica spanning the period 1979-2010 are classified into 16 different patterns (6 for summer and autumn and 10 for winter and spring). Each of these patterns has an atmospheric temperature and pressure structure associated with it (i.e., a specific mode of climate variability). These results were obtained using principal component analysis (PCA) in T-Mode. Here we attempt to identify the sea ice pattern for 2011 without using the passive microwave data, and instead using what can be inferred from the temperature and pressure fields associated with the patterns. We approach this issue with a multilayer Perceptron (neuronal network) with supervised learning and a back-propagation algorithm. The Perceptron is the most common Artificial Neural Network topology dedicated to image pattern recognition. It was implemented through the use of temperature and pressure anomalies field images that were associated with a group of sea ice anomaly patterns. The variables analyzed included only composites of surface air temperature and pressure to simplify the density of input data and avoid a non-converging solution (monthly input data for the period 1979-2010). The results of this analysis can be used to identify the sea ice patterns without the need of a new PCA analysis of the sea ice data. The anticipated outcome of this study is to obtain an accurate prediction of sea ice and perform a forecast. The findings may be vital to a more accurate planning of future missions to coastal Antarctic bases.

Orquera, F. J.; Torchio, M. A.; Barreira, S.

2012-12-01

402

Pattern recognition analysis in complex molecule synthesis and the preparation of iso-Diels-Alder motifs  

PubMed Central

The identification of synthesizable substructural domains within more complex structural targets is of significant value in designing a workable plan of synthesis. We term this process “pattern recognition analysis” (PRA). In this paper we continued to build on the theme of PRA as a potential resource in retrosynthetic blueprints to reach highly challenging targets. The paper operates at two levels. First, there is provided a clear sense of definitions of categories by which patterns are related to hypothetical reaction types. Although the required reaction type may for the moment not exist, we believe that this method of analysis is likely to promote innovation that identifies unmet needs and opportunities to advance the cause of complex target synthesis. In addition, we describe reductions to practice in expanding the menu of achievable patterns. It is likely that the future value of PRA will be associated with its utility in leading the way to new and exploitable chemical innovation.

Peng, Feng; Grote, Robin E.; Wilson, Rebecca M.; Danishefsky, Samuel J.

2013-01-01

403

A local-sky star recognition algorithm based on rapid triangle pattern index for ICCD images  

NASA Astrophysics Data System (ADS)

Local-sky star recognition algorithm is a process of recognizing the extracted stars in image by making use of the prior rough attitude of star sensor in celestial sphere. In order to improve the detection and response performance of star sensor working in dynamic condition, ICCD is applied to imaging stars. However, image taken by ICCD has more non-Gaussian noise and the energy of imaging star is unstable. So a local-sky star recognition algorithm using spatial triangular relationship as matching features is supposed to deal with the difficulties. In the first place, an index array is designed according to Guide Triangles, which is applied to construct Guide Triangle Index List. In the second place, a general directing range of star sensor boresight is calculated according to FOV of star sensor and the output of inertial guidance system, and then, the candidate Guide Triangles set in above region is obtained rapidly. In the third place, construct image triangle patterns by applying position and energy of the extracted stars in the image, and then match the image triangle patterns with the above candidate Guide Triangles set for two stages, until N(N>=2) groups of successfully matched triangles pairs with smallest matching deviations sum are obtained. At the last, the recognized Guide Stars have to be matched posterior referring to the principle of simulated sky image, and the recognition results of image stars are all obtained. The proposed algorithm has compact Guide Database structure, rapid local-sky guide triangles obtaining, and good recognition correction percentage, even it has worse star location precision and more false stars. The simulation tests are performed to validate the relative efficiency and adaptation of the algorithm.

Zhang, Wei; Qi, Sheng-xiang; Zhang, Rui; Yang, Lili; Sun, Ji-fu; Song, Li-quan; Tian, Jin-wen

2013-09-01

404

Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module.  

National Technical Information Service (NTIS)

Optical character recognition (OCR) is the process of converting an image of a document into text. While progress in OCR research has enabled low error rates for English text in low-noise images, performance is still poor for noisy images and documents in...

B. Kjersten

2011-01-01

405

Robust target recognition based on fractal analysis  

Microsoft Academic Search

Fractal image processing technology has been recognized as having great potential in automatic target recognition (ATR) and image compression. In this paper, Physical Optics Corporation demonstrates the feasibility of using a fractal image processing technique as a new and efficient approach for signature, pattern, and object recognition. Using optical Fourier transform and a ring-wedge detection technique, we generate and measure

Judy Chen; Andrew A. Kostrzewski; Dai H. Kim; Gajendra D. Savant; Jeongdal Kim; Anatoly A. Vasiliev

1997-01-01

406

Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques  

NASA Astrophysics Data System (ADS)

Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often not considered. This paper considers the performance of major fuzzy, neural, and neuro-fuzzy pattern recognition algorithms and compares their performances with common statistical methods for the same data sets. For the specific data sets chosen namely the Iris data set, an the small Soybean data set, two neuro-fuzzy algorithms, AFLC and IAFC, outperform other well- known fuzzy, neural, and neuro-fuzzy algorithms in minimizing the classification error and equal the performance of the Bayesian classification. AFLC, and IAFC also demonstrate excellent learning vector quantization capability in generating optimal code books for coding and decoding of large color images at very low bit rates with exceptionally high visual fidelity.

Mitra, Sunanda; Castellanos, Ramiro

1998-10-01

407

P2K-1 Acoustooptic Processing for Recognition of Optical Binary-Phase-Shift-Keying Codes for Photonic Routers  

Microsoft Academic Search

Optical processing with efficient coding is expected in photonic label routing network. We consider optical codes encoded in the time and spectral domains. In this study, collinear acoustooptic (AO) switches are investigated as a constituent elements of a wavelength selective correlator for optical BPSK codes. Crosstalk in code recognition is discussed with numerical analysis considering AO filtering characteristics for optical

Nobuo Goto; Yasumitsu Miyazaki

2006-01-01

408

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation  

PubMed Central

Background Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. Results We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. Conclusions BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

2013-01-01

409

Team activity analysis and recognition based on Kinect depth map and optical imagery techniques  

NASA Astrophysics Data System (ADS)

Kinect cameras produce low-cost depth map video streams applicable for conventional surveillance systems. However, commonly applied image processing techniques are not directly applicable for depth map video processing. Kinect depth map images contain range measurement of objects at expense of having spatial features of objects suppressed. For example, typical objects' attributes such as textures, color tones, intensity, and other characteristic attributes cannot be fully realized by processing depth map imagery. In this paper, we demonstrate application of Kinect depth map and optical imagery for characterization of indoor and outdoor group activities. A Casual-Events State Inference (CESI) technique is proposed for spatiotemporal recognition and reasoning of group activities. CESI uses an ontological scheme for representation of casual distinctiveness of a priori known group activities. By tracking and serializing distinctive atomic group activities, CESI allows discovery of more complex group activities. A Modified Sequential Hidden Markov Model (MS-HMM) is implemented for trail analysis of atomic events representing correlated group activities. CESI reasons about five levels of group activities including: Merging, Planning, Cooperation, Coordination, and Dispersion. In this paper, we present results of capability of CESI approach for characterization of group activities taking place both in indoor and outdoor. Based on spatiotemporal pattern matching of atomic activities representing a known group activities, the CESI is able to discriminate suspicious group activity from normal activities. This paper also presents technical details of imagery techniques implemented for detection, tracking, and characterization of atomic events based on Kinect depth map and optical imagery data sets. Various experimental scenarios in indoors and outdoors (e.g. loading and unloading of objects, human-vehicle interactions etc.,) are carried to demonstrate effectiveness and efficiency of the proposed model for characterization of distinctive group activities.

Elangovan, Vinayak; Bandaru, Vinod K.; Shirkhodaie, Amir

2012-05-01

410

Pyrolysis-mass spectrometry/pattern recognition on a well-characterized suite of humic samples  

USGS Publications Warehouse

A suite of well-characterized humic and fulvic acids of freshwater, soil and plant origin was subjected to pyrolysis-mass spectrometry and the resulting data were analyzed by pattern recognition and factor analysis. A factor analysis plot of the data shows that the humic acids and fulvic acids can be segregated into two distinct classes. Carbohydrate and phenolic components are more pronounced in the pyrolysis products of the fulvic acids, and saturated and unsaturated hydrocarbons contribute more to the humic acid pyrolysis products. A second factor analysis plot shows a separation which appears to be based primarily on whether the samples are of aquatic or soil origin. ?? 1985.

MacCarthy, P.; DeLuca, S. J.; Voorhees, K. J.; Malcolm, R. L.; Thurman, E. M.

1985-01-01

411

REMOVAL OF SPECTRO-POLARIMETRIC FRINGES BY TWO-DIMENSIONAL PATTERN RECOGNITION  

SciTech Connect

We present a pattern-recognition-based approach to the problem of the removal of polarized fringes from spectro-polarimetric data. We demonstrate that two-dimensional principal component analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us, in principle, to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.

Casini, R.; Judge, P. G. [High Altitude Observatory, NCAR P.O. Box 3000, Boulder, CO 80307-3000 (United States); Schad, T. A. [Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ 85721 (United States)

2012-09-10

412

Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps  

NASA Astrophysics Data System (ADS)

Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. In addition to manual data inspection, seismogram interpretation requires therefore new processing utilities for event detection, signal classification and data visualization. The use of machine learning techniques automatises decision processes and reveals the statistical properties of data. This approach is becoming more and more important and valuable for large and complex seismic records. Unsupervised learning allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. This study applies an unsupervised pattern recognition approach for the discovery, imaging and interpretation of temporal patterns in seismic array recordings. For this purpose, the data is parameterized by feature vectors, which combine different real-valued wavefield attributes for short time windows. Standard seismic analysis tools are used as feature generation methods, such as frequency-wavenumber, polarization and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure. The application to continuous recordings of seismic signals from an active volcano (Mount Merapi, Java, Indonesia) shows that volcano-tectonic and rockfall events can be detected and distinguished by clustering the feature vectors. Similar results are obtained in terms of correctly classifying events compared to a previously implemented supervised classification system. Furthermore, patterns in the background wavefield, that is the 24-hr cycle due to human activity, are intuitively visualized by means of the SOM representation. Finally, we apply our technique to an ambient seismic vibration record, which has been acquired for local site characterization. Disturbing wavefield patterns are identified which affect the quality of Love wave dispersion curve estimates. Particularly at night, when the overall energy of the wavefield is reduced due to the 24-hr cycle, the common assumption of stationary planar surface waves can be violated.

Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

2010-09-01

413

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

NASA Astrophysics Data System (ADS)

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%.

M?ynarczuk, Mariusz; Górszczyk, Andrzej; ?lipek, Bart?omiej

2013-10-01

414

Facial expression recognition based on local binary patterns and kernel discriminant isomap.  

PubMed

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap). PMID:22163713

Zhao, Xiaoming; Zhang, Shiqing

2011-01-01

415

Pattern Recognition Receptors and Cytokines in Mycobacterium tuberculosis Infection--The Double-Edged Sword?  

PubMed Central

Tuberculosis, an infectious disease caused by Mycobacterium tuberculosis (Mtb), remains a major cause of human death worldwide. Innate immunity provides host defense against Mtb. Phagocytosis, characterized by recognition of Mtb by macrophages and dendritic cells (DCs), is the first step of the innate immune defense mechanism. The recognition of Mtb is mediated by pattern recognition receptors (PRRs), expressed on innate immune cells, including toll-like receptors (TLRs), complement receptors, nucleotide oligomerization domain like receptors, dendritic cell-specific intercellular adhesion molecule grabbing nonintegrin (DC-SIGN), mannose receptors, CD14 receptors, scavenger receptors, and FC? receptors. Interaction of mycobacterial ligands with PRRs leads macrophages and DCs to secrete selected cytokines, which in turn induce interferon-?- (IFN?-) dominated immunity. IFN? and other cytokines like tumor necrosis factor-? (TNF?) regulate mycobacterial growth, granuloma formation, and initiation of the adaptive immune response to Mtb and finally provide protection to the host. However, Mtb can evade destruction by antimicrobial defense mechanisms of the innate immune system as some components of the system may promote survival of the bacteria in these cells and facilitate pathogenesis. Thus, although innate immunity components generally play a protective role against Mtb, they may also facilitate Mtb survival. The involvement of selected PRRs and cytokines on these seemingly contradictory roles is discussed.

Hossain, Md. Murad; Norazmi, Mohd-Nor

2013-01-01

416

Illumination-invariant pattern recognition using fringe-adjusted joint transform correlator and monogenic signal  

NASA Astrophysics Data System (ADS)

The joint transform correlator (JTC) technique has shown attractive performance for real-time pattern recognition applications. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC (FJTC) yields remarkable promise for object recognition, and it has been shown that the FJTC produces a better correlation output than alternate JTCs under varying illumination conditions of the input scene; however, it has been found that the FJTC is not illumination invariant. Therefore, to alleviate this drawback of the FJTC, an illumination-invariant FJTC, based on combination of the fringe-adjusted filter (FAF) and the monogenic signal, is presented. The performance of the FJTC and the proposed local phase based FJTC technique in unknown input-image with varying illumination is investigated and compared. The proposed detection algorithm makes use of the monogenic signal from a two dimensional object region to extract the local phase information for assisting the FJTC robust to illumination effects. Experimental results show that by utilizing the monogenic phase information enables the FAF-based JTC to produce sharper correlation peaks and higher peak-to-clutter ratio compared to alternate JTCs. The proposed technique may be used as a real-time region-ofinterest identifier in wide-area surveillance for automatic object recognition when the target under very dark or bright condition that beyond human vision.

Sidike, Paheding; Asari, Vijayan K.; Alam, Mohammad S.

2014-03-01

417

Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap  

PubMed Central

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap).

Zhao, Xiaoming; Zhang, Shiqing

2011-01-01

418

Recognition of Optical Layered Binary Phase Shift Keying Labels Using Coherent Acoustooptic Processor for Hierarchical Photonic Routing  

Microsoft Academic Search

Wavelength-selective optical processing using collinear acoustooptic (AO) switches will be useful in photonic routing systems. We have studied the applications of this processing to optical label recognition in label routers. We propose the recognition of hierarchical routing labels encoded in the binary-phase-shift-keying (BPSK) format using an AO processor consisting of optical delay waveguides and parallel double-stage AO switches. Using wavelength-multiplexed

Nobuo Goto; Yasumitsu Miyazaki

2010-01-01

419

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position  

Microsoft Academic Search

A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network

Kunihiko Fukushima

1980-01-01

420

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

NASA Technical Reports Server (NTRS)

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

Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2010-01-01

421

Accuracy, security, and processing time comparisons of biometric fingerprint recognition system using digital and optical enhancements  

NASA Astrophysics Data System (ADS)

Fingerprint recognition is one of the most commonly used forms of biometrics and has been widely used in daily life due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Besides cost, issues related to accuracy, security, and processing time in practical biometric recognition systems represent the most critical factors that makes these systems widely acceptable. Accurate and secure biometric systems often require sophisticated enhancement and encoding techniques that burdens the overall processing time of the system. In this paper we present a comparison between common digital and optical enhancementencoding techniques with respect to their accuracy, security and processing time, when applied to biometric fingerprint systems.

Alsharif, Salim; El-Saba, Aed; Jagapathi, Rajendarreddy

2011-05-01

422

Weather maps classification over Greek domain based on isobaric line patterns. A pattern recognition approach  

NASA Astrophysics Data System (ADS)

The paper presents a semi-supervised weather classification method based on 850-hPa isobaric level maps. A preprocessing step is employed, where isolines of geopotential height are extracted from weather map images via an image processing procedure. ? feature extraction stage follows where two techniques are applied. The first technique implements phase space reconstruction, and yields multidimensional delay distributions. The second technique is based on chain code representation of signals, from which histogram features are derived. Similarity measures are used to compare multidimensional data and the k-means algorithm is applied in the final stage. The method is applied over the area of Greece, and the resulting catalogues are compared to a subjective classification for this area. Numerical experiments with datasets derived from the European Meteorological Bulletin archives exhibit an up to 91 % accurate agreement with the subjective weather patterns.

Zagouras, Athanassios; Argiriou, Athanassios A.; Economou, George; Fotopoulos, Spiros; Flocas, Helena A.

2013-11-01

423

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

SciTech Connect

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.

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

2006-04-21

424

Recognition of damage-associated molecular patterns related to nucleic acids during inflammation and vaccination  

PubMed Central

All mammalian cells are equipped with large numbers of sensors for protection from various sorts of invaders, who, in turn, are equipped with molecules containing pathogen-associated molecular patterns (PAMPs). Once these sensors recognize non-self antigens containing PAMPs, various physiological responses including inflammation are induced to eliminate the pathogens. However, the host sometimes suffers from chronic infection or continuous injuries, resulting in production of self-molecules containing damage-associated molecular patterns (DAMPs). DAMPs are also responsible for the elimination of pathogens, but promiscuous recognition of DAMPs through sensors against PAMPs has been reported. Accumulation of DAMPs leads to massive inflammation and continuous production of DAMPs; that is, a vicious circle leading to the development of autoimmune disease. From a vaccinological point of view, the accurate recognition of both PAMPs and DAMPs is important for vaccine immunogenicity, because vaccine adjuvants are composed of several PAMPs and/or DAMPs, which are also associated with severe adverse events after vaccination. Here, we review as the roles of PAMPs and DAMPs upon infection with pathogens or inflammation, and the sensors responsible for recognizing them, as well as their relationship with the development of autoimmune disease or the immunogenicity of vaccines.

Jounai, Nao; Kobiyama, Kouji; Takeshita, Fumihiko; Ishii, Ken J.

2012-01-01

425

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques  

NASA Astrophysics Data System (ADS)

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

426

Species recognition through wing interference patterns (WIPs) in Achrysocharoides Girault (Hymenoptera, Eulophidae) including two new species  

PubMed Central

Abstract Wing interference patterns (WIPs) are shown to be an important tool for species recognition in the genus Achrysocharoides Girault (Hymenoptera: Eulophidae). This is demonstrated by combining information from two previously published papers, comprising two cases of cryptic species, and by new material including the description of two new species, Achrysocharoides maieri and Achrysocharoides serotinae from North America. The cryptic species were initially separated through their distinct male WIPs. Subsequent analyses of the external morphology uncovered additional morphological differences supporting the original findings through WIPs, and biological data further strengthened the identity of these species. The new species described here also differ in their WIPs but the WIPs are similar in both sexes. Thus they provide a strong link between male and female and demonstrate that WIPs can also be useful for species recognition when the sexes are otherwise difficult to associate. Both new species are from Connecticut, USA, and were reared from Phyllonorycter propinquinella (Braun) (Lepidoptera: Gracillariidae) on black cherry (Prunus serotina); Achrysocharoides maieri has also been reared from Ph. nr crataegella on pin cherry (Prunus pensylvanica). To facilitate the identification of the new species they are included in a previously published key to North American species of Achrysocharoides. As a supplement to colourful WIPs we also demonstrate that grey scale images of uncoated wings from scanning electron microscopy can be used for visualization of the thickness distribution pattern in wing membranes.

Shevtsova, Ekaterina; Hansson, Christer

2011-01-01

427

Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition  

NASA Astrophysics Data System (ADS)

Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

2011-02-01

428

Recognition of disease-specific patterns in FT-IR spectra of human sera  

NASA Astrophysics Data System (ADS)

Vibrational spectra in the mid-IR region show significant and reproducible correlation with the disease state of the blood donor. When focusing our 'disease pattern recognition (DPR)' approach onto the example of diabetes mellitus we can clearly separate samples obtained from healthy volunteers from those samples which organized from diabetes patients. Furthermore, we are able to differentiate between samples of type-1 diabetics and type-2 diabetics. For disease pattern recognition we use linear and/or regularized discriminant analysis. In a binary, supervised classification of an pair of the three disease states: healthy, diabetes type-1 and diabetes type-2, we consistently achieve sensitivities and specificities >= 80 percent. By setting stricter bounds on the range of acceptable probabilities of belonging to a certain class, we obtain even higher values for the sensitivity and the specificity on the expense of the fraction of 'crisply' classified samples. Since we are able to simultaneously quantify the concentrations of biochemical serum components like glucose, cholesterol and triglycerides from the identical set of spectra with regression coefficients > 90 percent, our approach allows for a direct cross-link between the molecule-based and the disease-based interpretation of the spectra.

Petrich, Wolfgang H.; Dolenko, Brion; Frueh, Johanna; Greger, Helmut; Jacob, Stephan; Keller, Franz; Nikulin, Alexander; Otto, Matthias; Quarder, Ortrud; Somorjai, Raymond L.; Staib, Arnulf; Werner, Gerhard H.; Wielinger, Hans

2000-05-01

429

Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation  

PubMed Central

Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

Fernandez-Llatas, Carlos; Meneu, Teresa; Traver, Vicente; Benedi, Jose-Miguel

2013-01-01

430

Pattern recognition algorithms for density estimation of asphalt pavement during compaction: a simulation study  

NASA Astrophysics Data System (ADS)

This paper presents the application of artificial neural network (ANN) based pattern recognition to extract the density information of asphalt pavement from simulated ground penetrating radar (GPR) signals. This study is part of research efforts into the application of GPR to monitor asphalt pavement density during compaction. The main challenge is to eliminate the effect of roller-sprayed water on GPR signals during compaction and to extract density information accurately. A calibration of the excitation function was conducted to provide an accurate match between the simulated signal and the real signal. A modified electromagnetic mixing model was then used to calculate the dielectric constant of asphalt mixture with water. A large database of GPR responses was generated from pavement models having different air void contents and various surface moisture contents using finite-difference time-domain simulation. Feature extraction was performed to extract density-related features from the simulated GPR responses. Air void contents were divided into five classes representing different compaction statuses. An ANN-based pattern recognition system was trained using the extracted features as inputs and air void content classes as target outputs. Accuracy of the system was tested using test data set. Classification of air void contents using the developed algorithm is found to be highly accurate, which indicates effectiveness of this method to predict asphalt concrete density.

Shangguan, Pengcheng; Al-Qadi, Imad L.; Lahouar, Samer

2014-08-01

431

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques.  

PubMed

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources. PMID:23257334

Palacios-Morillo, Ana; Alcázar, Angela; de Pablos, Fernando; Jurado, José Marcos

2013-02-15

432

Pattern Recognition via the Toll-Like Receptor System in the Human Female Genital Tract  

PubMed Central

The mucosal surface of the female genital tract is a complex biosystem, which provides a barrier against the outside world and participates in both innate and acquired immune defense systems. This mucosal compartment has adapted to a dynamic, non-sterile environment challenged by a variety of antigenic/inflammatory stimuli associated with sexual intercourse and endogenous vaginal microbiota. Rapid innate immune defenses against microbial infection usually involve the recognition of invading pathogens by specific pattern-recognition receptors recently attributed to the family of Toll-like receptors (TLRs). TLRs recognize conserved pathogen-associated molecular patterns (PAMPs) synthesized by microorganisms including bacteria, fungi, parasites, and viruses as well as endogenous ligands associated with cell damage. Members of the TLR family, which includes 10 human TLRs identified to date, recognize distinct PAMPs produced by various bacterial, fungal, and viral pathogens. The available literature regarding the innate immune system of the female genital tract during human reproductive processes was reviewed in order to identify studies specifically related to the expression and function of TLRs under normal as well as pathological conditions. Increased understanding of these molecules may provide insight into site-specific immunoregulatory mechanisms in the female reproductive tract.

Nasu, Kaei; Narahara, Hisashi

2010-01-01

433

Nucleic acids and endosomal pattern recognition: how to tell friend from foe?  

PubMed Central

The innate immune system has evolved endosomal and cytoplasmic receptors for the detection of viral nucleic acids as sensors for virus infection. Some of these pattern recognition receptors (PRR) detect features of viral nucleic acids that are not found in the host such as long stretches of double-stranded RNA (dsRNA) and uncapped single-stranded RNA (ssRNA) in case of Toll-like receptor (TLR) 3 and RIG-I, respectively. In contrast, TLR7/8 and TLR9 are unable to distinguish between viral and self-nucleic acids on the grounds of distinct molecular patterns. The ability of these endosomal TLR to act as PRR for viral nucleic acids seems to rely solely on the mode of access to the endolysosomal compartment in which recognition takes place. The current dogma states that self-nucleic acids do not enter the TLR-sensing compartment under normal physiological conditions. However, it is still poorly understood how dendritic cells (DC) evade activation by self-nucleic acids, in particular with regard to specific DC subsets, which are specialized in taking up material from dying cells for cross-presentation of cell-associated antigens. In this review we discuss the current understanding of how the immune system distinguishes between foreign and self-nucleic acids and point out some of the key aspects that still require further research and clarification.

Brencicova, Eva; Diebold, Sandra S.

2013-01-01

434

Application of hierarchical clustering technique for numerical tectonic regionalization of the Zagros (Iran): an unsupervised pattern recognition  

NASA Astrophysics Data System (ADS)

Hierarchical clustering technique is fundamentally a method data exploration and identifying the patterns inherent in a data set. This numerical pattern recognition technique involves the recognition of pattern and grouping structure in data where no a priori classification exists. This paper attempts to present a general quantitative pattern based on hierarchical clustering technique for tectonic regionalization of the Zagros in order to presenting and demonstrating the ability of this clustering method as an unsupervised pattern recognition technique to perform a numerical tectonic zoning study. For this purpose, after preparation the multivariate data matrix containing 137 sub-areas and 18 quantitative variables, the relationships among the sub-areas were obtained through cluster analysis using Ward's method, and Euclidean distance as similarity measure. The results, synthesized in a dendrogram, were used for providing a series of automated tectonic zoning maps of the region produced in different levels of similarities that show trends in tectonic evolution of it. In general, the automated tectonic zoning maps presented in this study show good agreement with the overall geological and quantitative works. However, in the present study some new findings about the tectonic nature of the region have been obtained. This study simply presents the necessity and usefulness of hierarchical cluster analysis, as an appropriate statistical pattern recognition technique, for increasing the degree of the objectivity of the regionalization researches in the Earth sciences. Also, it introduces this analytical method a very powerful technique for a meaningful data reduction and interpreting the data and indicate many hidden patterns.

Zadeh, Rezvan Mehdi; Naser Hashemi, Seyed

2010-05-01

435

Real-time intelligent pattern recognition algorithm for surface EMG signals  

PubMed Central

Background Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS) integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD) and time-frequency representation (TFR). Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA) is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time-frequency domain. In this work we demonstrate the capability of an EMG pattern recognition system using ANFIS as classifier with a real-time learning method. Our results reveal that the utilized real-time ANFIS approach along with the user evaluation provides a 96.7% average accuracy. This rate is superior to the previously reported result utilizing artificial neural networks (ANN) real-time method [1]. Conclusion This study shows that ANFIS real-time learning method coupled with mixed time and time-frequency features as EMG features can provide acceptable results for designing sEMG pattern recognition system suitable for hand prosthesis control.

Khezri, Mahdi; Jahed, Mehran

2007-01-01

436

Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images.  

PubMed

Methods for numerical description and subsequent classification of cellular protein localization patterns are described. Images representing the localization patterns of 4 proteins and DNA were obtained using fluorescence microscopy and divided into distinct training and test sets. The images were processed to remove out-of-focus and background fluorescence and 2 sets of numeric features were generated: Zernike moments and Haralick texture features. These feature sets were used as inputs to either a classification tree or a neural network. Classifier performance (the average percent of each type of image correctly classified) on previously unseen images ranged from 63% for a classification tree using Zernike moments to 88% for a backpropagation neural network using a combination of features from the 2 feature sets. These results demonstrate the feasibility of applying pattern recognition methods to subcellular localization patterns, enabling sets of previously unseen images from a single class to be classified with an expected accuracy greater than 99%. This will provide not only a new automated way to describe proteins, based on localization rather than sequence, but also has potential application in the automation of microscope functions and in the field of gene discovery. PMID:9822349

Boland, M V; Markey, M K; Murphy, R F

1998-11-01

437

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.  

PubMed

On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes (which information is explicitly present in input data streams and would be crucial to consider in some tasks) and of the information contained in the timing of the following spikes that is learned by the dynamic synapses as a whole spatio-temporal pattern. PMID:23340243

Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

2013-05-01

438

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.  

PubMed

Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ?8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). PMID:24721224

Khushaba, Rami N; Takruri, Maen; Miro, Jaime Valls; Kodagoda, Sarath

2014-07-01

439

Self-organizing hierarchic networks for pattern recognition in protein sequence.  

PubMed Central

We present a method based on hierarchical self-organizing maps (SOMs) for recognizing patterns in protein sequences. The method is fully automatic, does not require prealigned sequences, is insensitive to redundancy in the training set, and works surprisingly well even with small learning sets. Because it uses unsupervised neural networks, it is able to extract patterns that are not present in all of the unaligned sequences of the learning set. The identification of these patterns in sequence databases is sensitive and efficient. The procedure comprises three main training stages. In the first stage, one SOM is trained to extract common features from the set of unaligned learning sequences. A feature is a number of ungapped sequence segments (usually 4-16 residues long) that are similar to segments in most of the sequences of the learning set according to an initial similarity matrix. In the second training stage, the recognition of each individual feature is refined by selecting an optimal weighting matrix out of a variety of existing amino acid similarity matrices. In a third stage of the SOM procedure, the position of the features in the individual sequences is learned. This allows for variants with feature repeats and feature shuffling. The procedure has been successfully applied to a number of notoriously difficult cases with distinct recognition problems: helix-turn-helix motifs in DNA-binding proteins, the CUB domain of developmentally regulated proteins, and the superfamily of ribokinases. A comparison with the established database search procedure PROFILE (and with several others) led to the conclusion that the new automatic method performs satisfactorily.

Hanke, J.; Beckmann, G.; Bork, P.; Reich, J. G.

1996-01-01

440

Optical Signal Processing.  

National Technical Information Service (NTIS)

Optical signal processing makes possible rapid coordinate transformations, optical pattern recognition, and matrix-matrix multiplication. In the present contract, DSI has demonstrated several significant accomplishments. Among these are: (a) the design an...

1983-01-01

441

Optical Imaging of Flow Pattern and Phantom  

NASA Technical Reports Server (NTRS)

Time-resolved optical imaging technique has been used to image the spatial distribution of small droplets and jet sprays in a highly scattering environment. The snake and ballistic components of the transmitted pulse are less scattered, and contain direct information about the sample to facilitate image formation as opposed to the diffusive components which are due to multiple collisions as a light pulse propagates through a scattering medium. In a time-gated imaging scheme, these early-arriving, image-bearing components of the incident pulse are selected by opening a gate for an ultrashort period of time and a shadowgram image is detected. Using a single shot cooled CCD camera system, the formation of water droplets is monitored as a function of time. Picosecond time-gated image of drop in scattering cells, spray droplets as a function of let speed and gas pressure, and model calcification samples consisted of calcium carbonate particles of irregular shapes ranging in size from 0. 1 to 1.5 mm affixed to a microscope slide have been measured. Formation produced by an impinging jet will be further monitored using a CCD with 1 kHz framing illuminated with pulsed light. The desired image resolution of the fuel droplets is on the 20 pm scale using early light through a highly scattering medium. A 10(exp -6)m displacement from a jet spray with a flow speed of 100 m/sec introduced by the ns grating pulse used in the imaging is negligible. Early ballistic/snake light imaging offers nondestructive and noninvasive method to observe the spatial distribution of hidden objects inside a highly scattering environment for space, biomedical, and materials applications. In this paper, the techniques we will present are time-resolved K-F transillumination imaging and time-gated scattered light imaging. With a large dynamic range and high resolution, time-gated early light imaging has the potential for improving rocket/aircraft design by determining jets shape and particle sizes. Refinements to these techniques may enable drop size measurements in the highly scattering, optically dense region of multi-element rocket injectors. These types of measurements should greatly enhance the design of stable, and higher performing rocket engines.

Galland, Pierre A.; Liang, X.; Wang, L.; Ho, P. P.; Alfano, R. R.; Breisacher, K.

1999-01-01

442

Relative performance of clustering-based neural network and statistical pattern recognition models for nondestructive damage detection  

NASA Astrophysics Data System (ADS)

The objective of this paper is to compare and contrast the capabilities of neural networks and statistical pattern recognition to localize damage in three-dimensional structures. A theory of damage localization, which yields information on the location of the damage directly from changes in mode shapes, is formulated. Next, the application of statistical pattern recognition and neural networks for nondestructive damage detection (NDD) is established. Expressions for classification using linear discriminant functions and a two-stage supervised clustering-based neural network are generated. Damage localization is applied to a finite-element model (FEM) of a structure which contains simulated damage at various locations. A set of criteria for comparing and contrasting statistical pattern recognition and neural network models is then established. Finally, the evaluation of the two models is carried out using the established criteria.

Garcia, Gabe; Butler, Karen; Stubbs, Norris

1997-08-01

443

Laser illuminator and optical system for disk patterning  

DOEpatents

Magnetic recording media are textured over areas designated for contact in order to minimize friction with data transducing heads. In fabricating a hard disk, an aluminum nickel-phosphorous substrate is polished to a specular finish. A mechanical means is then used to roughen an annular area intended to be the head contact band. An optical and mechanical system allows thousands of spots to be generated with each laser pulse, allowing the textured pattern to be rapidly generated with a low repetition rate laser and an uncomplicated mechanical system. The system uses a low power laser, a beam expander, a specially designed phase plate, a prism to deflect the beam, a lens to transmit the diffraction pattern to the far field, a mechanical means to rotate the pattern and a trigger system to fire the laser when sections of the pattern are precisely aligned. The system generates an annular segment of the desired pattern with which the total pattern is generated by rotating the optical system about its optic axis, sensing the rotational position and firing the laser as the annular segment rotates into the next appropriate position. This marking system can be integrated into a disk sputtering system for manufacturing magnetic disks, allowing for a very streamlined manufacturing process.

Hackel, Lloyd A. (Livermore, CA); Dane, C. Brent (Livermore, CA); Dixit, Shamasundar N. (Livermore, CA); Everett, Mathew (Pleasanton, CA); Honig, John (Livermore, CA)

2000-01-01

444

(Spectral) pattern recognition as a versatile tool towards automatic landmine detection: A new European approach  

NASA Astrophysics Data System (ADS)

A mobile acousto-optical sensor (Laser Vibrometer) is being used for the detection and discrimination of buried landmines. Analysis of measurement data, obtained in a number of field tests, reveals that buried mines (anti-tank mines as well as anti personnel mines) can reliably be discriminated from nonletha