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Journal articles on the topic 'Pattern recognition systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Rizki, M. M., M. A. Zmuda, and L. A. Tamburino. "Evolving pattern recognition systems." IEEE Transactions on Evolutionary Computation 6, no. 6 (December 2002): 594–609. http://dx.doi.org/10.1109/tevc.2002.806167.

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2

ZAGORUIKO, N. G. "EXPERT SYSTEMS AND PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (March 1989): 1–7. http://dx.doi.org/10.1142/s0218001489000024.

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A definition of expert systems is given, its pragmatic demands are cited and its structure is described. The methods and ways of pattern recognition are used in the subsystems DIALOGUE, ANALYTIC and HOMEOSTAT. The recognition algorithms which work on the information to be retained in the data base and knowledge base are described. The problems of recognition appearing under the construction of expert systems are noted.

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3

Lapko, A. V., and V. A. Lapko. "Hybrid systems of pattern recognition." Pattern Recognition and Image Analysis 18, no. 1 (January 2008): 7–13. http://dx.doi.org/10.1134/s1054661808010021.

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4

Fred, Ana, and Anil K. Jain. "Pattern recognition in information systems." Pattern Recognition 35, no. 12 (December 2002): 2671–72. http://dx.doi.org/10.1016/s0031-3203(02)00094-8.

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5

POTAPOV, ALEXEI B., and M. K. ALI. "PATTERN RECOGNITION WITH HAMILTONIAN DYNAMICS." International Journal of Modern Physics C 12, no. 05 (June 2001): 751–58. http://dx.doi.org/10.1142/s0129183101001948.

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We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.

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6

Pham, D. T., and E. Oztemel. "Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 2 (May 1993): 113–18. http://dx.doi.org/10.1243/pime_proc_1993_207_325_02.

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Pattern recognition systems made up of independent multi-layer perceptrons and learning-vector-quantization neural network modules have been developed for classifying control chart patterns. These composite pattern recognition systems have better classification capabilities than their individual modules. The paper describes the structures of these pattern recognition systems and the results obtained on using them.

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7

Gray, J. O., and M. L. Sanderson. "Parallel Processing for Pattern Recognition Systems." IFAC Proceedings Volumes 19, no. 9 (June 1986): 155–58. http://dx.doi.org/10.1016/s1474-6670(17)57523-0.

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8

Ciaccio, E. J., S. M. Dunn, and M. Akay. "Biosignal pattern recognition and interpretation systems." IEEE Engineering in Medicine and Biology Magazine 12, no. 3 (September 1993): 89–95. http://dx.doi.org/10.1109/51.232348.

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9

Hassan, Maguid H. M. "Smart pattern recognition of structural systems." Smart Structures and Systems 6, no. 1 (January 25, 2010): 39–56. http://dx.doi.org/10.12989/sss.2010.6.1.039.

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10

Giakoumakis, E., G. Papaconstantinou, and E. Skordalakis. "Rule-based systems and pattern recognition." Pattern Recognition Letters 5, no. 4 (April 1987): 267–72. http://dx.doi.org/10.1016/0167-8655(87)90056-0.

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11

Sapaty, P. S. "Managing distributed systems with spatial grasp patterns." Mathematical machines and systems 4 (2023): 11–25. http://dx.doi.org/10.34121/1028-9763-2023-4-11-25.

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The pattern is everything around us. It can represent the world’s regularity, a human-made design, a model, plan, diagram, a standard way of modeling, acting and thinking, a distinctive style or form, a combination of qualities and tendencies, etc. That is why the theory, research, and practical works on patterns are so important for different scientific and technological fields, having also stimulated the preparation and writing of the current paper. The paper reviews existing works on patterns, grouping them by different categories, and briefs the developed Spatial Grasp Model and Technology (SGT) and its Spatial Grasp Language (SGL) with the distributed networked implementation, which provide effective distributed solutions in systems management, control, and simulation by active self-spreading patterns. The article shows how practical patterns can be expressed in SGL, including regular patterns, patterns of concrete objects, and different pattern-based management solutions like coordinating transport columns, finding distributed zone coordinates, and spatial tracking of mobile objects. It also gives network examples of distributed pattern recognition and matching with the use of self-propagating active network templates reflecting images to be found. The paper provides a classified summary of the investigated use of SGL for pattern operations in different areas, which includes descriptive patterns, creative patterns, patterns as spatial processes, pattern recognition, self-matching patterns, combined patterns, cooperating and conflicting patterns, psychological patterns, and recursive patterns. The work concludes with the belief that SGL can be used as a real, very effective, and compact language for pattern representation and operations, and SGT should contribute to the pattern theory and resultant technologies.

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12

Tan, Z., B. S. Hepburn, C. Tucker, and M. K. Ali. "Pattern recognition using chaotic neural networks." Discrete Dynamics in Nature and Society 2, no. 4 (1998): 243–47. http://dx.doi.org/10.1155/s1026022698000211.

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Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.

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13

de Ridder, D., J. de Ridder, and M. J. T. Reinders. "Pattern recognition in bioinformatics." Briefings in Bioinformatics 14, no. 5 (April 4, 2013): 633–47. http://dx.doi.org/10.1093/bib/bbt020.

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14

HAKEN, HERMANN. "SYNERGETICS: FROM PATTERN FORMATION TO PATTERN ANALYSIS AND PATTERN RECOGNITION." International Journal of Bifurcation and Chaos 04, no. 05 (October 1994): 1069–83. http://dx.doi.org/10.1142/s0218127494000782.

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It is by now well known that numerous open systems in physics (fluids, plasmas, lasers, nonlinear optical devices, semiconductors), chemistry and biology (morphogenesis) may spontaneously develop spatial, temporal or spatiotemporal structures by self-organization. Quite often, striking analogies between the corresponding patterns can be observed in spite of the fact that the underlying systems are of quite a different nature. In this paper I shall first give an outline of general concepts that allow us to deal with the spontaneous formation of structures from a unifying point of view that is based on concepts of instability, order parameters and enslavement. We shall discuss a number of generalized Ginzburg-Landau equations. In most cases treated so far, theory started from microscopic or mesoscopic equations of motion from which the evolving structures were derived. In my paper I shall address two further problems that are in a way the reverse, namely (1) Can we derive order parameters and the basic modes from observed experimental data? (2) Can we construct systems by means of an underlying dynamics that are capable of producing patterns or structures that we prescribe? In order to address (1), a new variational principle that may be derived from path intergrals is introduced and illustrated by examples. An approach to the problem (2) is illustrated by the device of a computer that recognizes patterns and that may be realized by various kinds of spontaneous pattern formations in semiconductors and lasers.

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15

Hjelmfelt, A., F. W. Schneider, and J. Ross. "Pattern Recognition in Coupled Chemical Kinetic Systems." Science 260, no. 5106 (April 16, 1993): 335–37. http://dx.doi.org/10.1126/science.260.5106.335.

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16

Tamburino, L. A., M. A. Zmuda, and M. M. Rizki. "Generating pattern-recognition systems using evolutionary learning." IEEE Expert 10, no. 4 (August 1995): 63–68. http://dx.doi.org/10.1109/64.403962.

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17

Kempf, Roland, and Jürgen Adamy. "Sequential pattern recognition employing recurrent fuzzy systems." Fuzzy Sets and Systems 146, no. 3 (September 2004): 451–72. http://dx.doi.org/10.1016/j.fss.2003.08.007.

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18

Naveen, V. Jagan, K. Krishna Kishore, and P. Rajesh Kumar. "Human Ear Pattern Recognition System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 117. http://dx.doi.org/10.23956/ijarcsse.v7i8.35.

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In the modern world, human recognition systems play an important role to improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm. Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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19

Robertson, Graham, and Ian Craw. "Testing face recognition systems." Image and Vision Computing 12, no. 9 (November 1994): 609–14. http://dx.doi.org/10.1016/0262-8856(94)90014-0.

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20

Emptoz, H., and M. Lamure. "A systemic approach to pattern recognition." Robotica 5, no. 2 (April 1987): 129–33. http://dx.doi.org/10.1017/s0263574700015095.

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SUMMARYWe suggest a new pretopological model for pattern recognition which was introduced to study complex economic systems. The model has its origin in the concept of “neighbour”, which is both primitive and fundamental in pattern recognition. Pretopology enables us to develop a perceptive and topological approach for patterns and to see that problems, apparently different, are in fact identical e.g. clustering and recognition, search of skeletons in image processing and search of an informative learning set. It should be noted that the suggeted model is more than a descriptive one.

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21

Moolchandani, Jhankar, and Kulvinder Singh. "English language analysis using pattern recognition and machine learning." Scientific Temper 14, no. 03 (September 27, 2023): 774–81. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.32.

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Pattern identification and classification in complicated systems are difficult. This study uses optical character recognition (OCR) to digitize handwritten data. OCR segments and categorizes characters using online and offline methods for different input sources. Hindi and Bangladeshi categorization results unite linguistic studies. Handwriting recognition systems create editable digital documents from touchscreens, electronic pens, scanners, and photographs. Statistical, structural, neural network and syntactic methods improve online and offline recognition. In “english language analysis using pattern recognition and machine learning,” the accuracy of various approaches is examined, showing deep convolution neural networks (DCNN) 98% accuracy in recognizing subtle linguistic patterns. Nave Bayes, a trustworthy language analysis approach, has 96.2% accuracy. Table recognition (TR) algorithms retrieve structured information at 97%. This method outperforms others with 98.4% accuracy. This unique strategy could improve english language analysis using cutting-edge pattern recognition and machine learning techniques.

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22

Zhu, Erkang, Silu Huang, and Surajit Chaudhuri. "High-Performance Row Pattern Recognition Using Joins." Proceedings of the VLDB Endowment 16, no. 5 (January 2023): 1181–95. http://dx.doi.org/10.14778/3579075.3579090.

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The SQL standard introduced MATCH_RECOGNIZE in 2016 for row pattern recognition. Since then, MATCH_RECOGNIZE has been supported by several leading relation systems, they implemented this function using Non-Deterministic Finite Automaton (NFA). While NFA is suitable for pattern recognition in streaming scenarios, the current uses of NFA by the relational systems for historical data analysis scenarios overlook important optimization opportunities. We propose a new approach to use Join to speed up row pattern recognition in historical analysis scenarios for relational systems. Implemented as a logical plan rewrite rule, the new approach first filters the input relation to MATCH_RECOGNIZE using Joins constructed based on a subset of symbols taken from the PATTERN expression, then run the NFA-based MATCH_RECOGNIZE on the filtered rows, reducing the net cost. The rule also includes a specialized cardinality model for the Joins and a cost model for the NFA-based MATCH_RECOGNIZE operator for choosing an appropriate symbol set. The rewrite rule is applicable when the query pattern's definition is self-contained and either the input table has no duplicates or there is a window condition. Applying the rewrite rule to a query benchmark with 1,800 queries spanning over 6 patterns and 3 pattern definitions, we observed median speedups of 5.4X on Trino (v373 with ORC files on Hive), 57.5X on SQL Server (2019) using column store and 41.6X on row store.

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23

Urunkar, Abhishek, Sudhanshu Khapre, and Mayuri Kasabe. "Pattern Recognition in Embedded Systems for Event Occurrences." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 1121–28. http://dx.doi.org/10.22214/ijraset.2024.58523.

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Abstract: PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN)suggests two deep face detection methods. These are presented to use the deep learning algorithm.This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.

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24

HALAVATI, RAMIN, and SAEED BAGHERI SHOURAKI. "RECOGNITION OF PERSIAN ONLINE HANDWRITING USING ELASTIC FUZZY PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 03 (May 2007): 491–513. http://dx.doi.org/10.1142/s0218001407005533.

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Persian is a fully cursive handwriting in which each character may take different forms in different parts of the word, characters overlap and there is a wide range of possible styles. These complexities make automatic recognition of Persian a very hard task. This paper presents a novel approach on recognition of such writings systems which is based on the description of input stream by a sequence of fuzzy linguistic terms; representation of character patterns with the same descriptive language; and comparison of inputs with character patterns using a novel elastic pattern matching approach. As there is no general benchmark for recognition of Persian handwriting, the approach has been tested on the set of words in first primary Iranian school books including 1250 words resulting in 78% correct recognition without dictionary and 96% with dictionary.

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25

Inkinen, Sami J. "Pattern-Recognition Transforms." Signal Processing 35, no. 1 (January 1994): 100–102. http://dx.doi.org/10.1016/0165-1684(94)90201-1.

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26

Bouguelid, Mohamed Saïd, Moamar Sayed Mouchaweh, Patrice Billaudel, and Bernard Riera. "HYBRID PATTERN RECOGNITION METHOD TO DIAGNOSE DYNAMIC SYSTEMS." IFAC Proceedings Volumes 40, no. 16 (2007): 51–56. http://dx.doi.org/10.3182/20070904-3-kr-2922.00009.

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27

Yoshida, Norihiko, and Kouji Hino. "An object-oriented framework of pattern recognition systems." ACM SIGPLAN Notices 23, no. 11 (November 1988): 259–67. http://dx.doi.org/10.1145/62084.62106.

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28

TAMBURINO, LOUIS A., and MATEEN M. RIZKI. "PERFORMANCE-DRIVEN AUTONOMOUS DESIGN OF PATTERN-RECOGNITION SYSTEMS." Applied Artificial Intelligence 6, no. 1 (January 1992): 59–77. http://dx.doi.org/10.1080/08839519208949942.

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29

Ortega, Arturo, Santiago Marco, Teodor Šundic, and Josep Samitier. "New pattern recognition systems designed for electronic noses." Sensors and Actuators B: Chemical 69, no. 3 (October 2000): 302–7. http://dx.doi.org/10.1016/s0925-4005(00)00511-6.

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30

Burke, Laura Ignizio. "Introduction to artificial neural systems for pattern recognition." Computers & Operations Research 18, no. 2 (January 1991): 211–20. http://dx.doi.org/10.1016/0305-0548(91)90091-5.

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31

Corbett, J., J. Fischer, and D. Whitney. "Averaging independent estimates improves pattern recognition." Journal of Vision 9, no. 8 (September 3, 2010): 819. http://dx.doi.org/10.1167/9.8.819.

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32

Wang, Yue, Lei Ren, Hongzhen Peng, Linjie Guo, and Lihua Wang. "DNA-Programmed Biomolecular Spatial Pattern Recognition." Chemosensors 11, no. 7 (June 27, 2023): 362. http://dx.doi.org/10.3390/chemosensors11070362.

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Molecular recognition based on non-covalent interactions between two or more molecules plays a crucial role in biological systems. Specific biological molecule recognition has been widely applied in biotechnology, clinical diagnosis, and treatment. The efficiency and affinity of molecular recognition are greatly determined by the spatial conformation of biomolecules. The designability of DNA nanotechnology makes possible the precise programming of the spatial conformation of biomolecules including valency and spacing, further achieving spatial pattern recognition regulation between biomolecules. This review summarizes recent achievements with DNA-based molecular spatial pattern recognition systems, the important factors affecting spatial pattern recognition, and their applications in biosensing, bioimaging, and targeted therapy. The future challenges in and development of this field are discussed and prospected. This review will provide valuable guidance for the creation of new DNA tools to enhance the efficiency and specificity of biomolecular recognition.

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Partila, Pavol, Miroslav Voznak, and Jaromir Tovarek. "Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/573068.

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

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34

Telksnys, Laimutis. "Self-Formation Supported by Pattern Recognition." Solid State Phenomena 97-98 (April 2004): 51–58. http://dx.doi.org/10.4028/www.scientific.net/ssp.97-98.51.

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Artificial planar systems self-formation process supported by pattern recognition theory and methods are discussed. Concept possibilities to apply pattern recognition power for improving and control self-formation processes are presented.

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35

Ahmadian, Kushan, and Marina Gavrilova. "Chaotic Neural Network for Biometric Pattern Recognition." Advances in Artificial Intelligence 2012 (August 30, 2012): 1–9. http://dx.doi.org/10.1155/2012/124176.

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Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.

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36

Mikhailov, I. A. "Some methods for pattern recognition." Automatic Control and Computer Sciences 44, no. 7 (December 2010): 420–25. http://dx.doi.org/10.3103/s0146411610070084.

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37

Fang, Yan, Victor V. Yashin, Steven P. Levitan, and Anna C. Balazs. "Pattern recognition with “materials that compute”." Science Advances 2, no. 9 (September 2016): e1601114. http://dx.doi.org/10.1126/sciadv.1601114.

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

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38

Rahman, F. "Nonlinear Dynamics of Neural Networks: Applications in Pattern Recognition." Communications on Applied Nonlinear Analysis 30, no. 3 (December 27, 2023): 16–31. http://dx.doi.org/10.52783/cana.v30.276.

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Improvements in pattern recognition systems are best achieved by fully comprehending and utilizing the nonlinear dynamics of neural networks. When dealing with complex, nonlinear patterns in real-world data, a nuanced approach is necessary to fully utilize neural networks' potential. By improving performance, interpretability, and flexibility in complicated pattern recognition tasks, Nonlinear Dynamics method meet this demand. Problems with interpretability, complicated training, and generalization in the presence of noisy data are all aspects of nonlinear dynamics that pose difficulties. These complex patterns are difficult for traditional neural networks to capture and comprehend. In response to these difficulties, Nonlinear Dynamics has been created to offer a comprehensive approach to making good use of nonlinear dynamics in pattern recognition. This research presents a novel method for dealing with problems caused by nonlinear dynamics, called Nonlinear Dynamics-Driven Adaptive Neural Network (ND-ANN). To achieve the sweet spot between model complexity and interpretability, NDANN employs a hybrid learning algorithm (HLA), an explainability module, ensemble integration, regularization for stability, and an adaptive architecture. This novel approach lays the groundwork for future developments in pattern recognition technology and guarantees better performance when capturing complicated patterns. Improved pattern recognition systems made possible by NDANN's accurate modeling of nonlinear dynamics pave the way for new developments in healthcare, information processing, and technology. Validation of NDANN's effectiveness in dealing with nonlinear dynamics issues was achieved through extensive simulation analyses. Precision, accuracy, recall, and F1 score are some of the performance indicators that undergo thorough evaluation across various datasets. The inclusion of nonlinear dynamics in neural networks has the ability to transform pattern recognition, and the simulation results show that NDANN is better than traditional models.

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39

Raghavan, Raghu. "Cellular automata in pattern recognition." Information Sciences 70, no. 1-2 (May 1993): 145–77. http://dx.doi.org/10.1016/0020-0255(93)90052-n.

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40

Lakatos, Stephen. "Recognition of Complex Auditory-Spatial Patterns." Perception 22, no. 3 (March 1993): 363–74. http://dx.doi.org/10.1068/p220363.

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Two experiments were carried out to investigate the perception of complex auditory-spatial patterns. Subjects were asked to identify alphanumeric characters whose patterns could be outlined acoustically through the sequential activation of specific units in a speaker array. Signal bandwidths were varied systematically in both experiments. Signals in experiment 1 had sharp onsets and offsets; envelope shapes in experiment 2 were much more gradual. Subjects showed considerable ability in recognizing alphanumeric patterns traced with signals of varying acoustical composition. Reductions in the steepness of signal attack and decay produced limited declines in pattern recognition ability. Systematic trends in the relation between patterns and the distribution of incorrect responses suggest that subjects performed a pattern-matching task, in which identifications were made on the basis of component features. The unexpected pattern recognition abilities that subjects demonstrated in both experiments suggest that spatial hearing, like vision, has access to mechanisms for amodal spatial representations.

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41

Jeong, Gu-Min, Hyun-Sik Ahn, Sang-Il Choi, Nojun Kwak, and Chanwoo Moon. "Pattern recognition using feature feedback: Application to face recognition." International Journal of Control, Automation and Systems 8, no. 1 (February 2010): 141–48. http://dx.doi.org/10.1007/s12555-010-0118-7.

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42

Mikhailov, A. M., M. F. Karavay, and V. A. Sivtsov. "Instantaneous Learning in Pattern Recognition." Automation and Remote Control 83, no. 3 (March 2022): 417–25. http://dx.doi.org/10.1134/s0005117922030092.

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43

Schrouff, J., M. J. Rosa, J. M. Rondina, A. F. Marquand, C. Chu, J. Ashburner, C. Phillips, J. Richiardi, and J. Mourão-Miranda. "PRoNTo: Pattern Recognition for Neuroimaging Toolbox." Neuroinformatics 11, no. 3 (February 16, 2013): 319–37. http://dx.doi.org/10.1007/s12021-013-9178-1.

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44

Duda, Peter D., and Jeffrey O. Adams. "Hemispheric Asymmetries for Complex Visual Patterns." Perceptual and Motor Skills 64, no. 2 (April 1987): 463–68. http://dx.doi.org/10.2466/pms.1987.64.2.463.

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Three tachistoscopic studies examined the laterality of spatial-form perception in normal adults using randomly generated eight-point and 12-point patterns (Vanderplas & Garvin, 1959) as the lateralized stimuli. In the first study of recognition accuracy, 36 subjects were tested in a partial replication of Fontenot. No laterality effects were found, and over-all recognition was better for the more complex 12-point patterns. In a second similar study with 20 subjects, the lateralized stimulus was followed by a central masking pattern. A left-hemisphere superiority for recognition and better over-all recognition for more complex patterns was obtained. These data do not support Fontenot's report of right-hemisphere superiority in complex visuospatial processing. Given these diverse findings, a reaction time study using mental rotation was conducted using the same patterns to determine whether latency would reflect accuracy of recognition. Twenty-six subjects judged whether a rotated lateralized test pattern was the same or different from a central target pattern. Measures of both latency and accuracy were separately assessed. No main effect of visual field was obtained on either measure. These studies suggest that the nature of hemispheric involvement in spatial form perception is far from resolved.

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Strasburger, Hans, Ingo Rentschler, and Martin Jüttner. "Peripheral vision and pattern recognition: A review." Journal of Vision 11, no. 5 (December 28, 2011): 13. http://dx.doi.org/10.1167/jov.10.1167/11.5.13.

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Wang, Yingxu. "On Visual Semantic Algebra (VSA)." International Journal of Software Science and Computational Intelligence 1, no. 4 (October 2009): 1–16. http://dx.doi.org/10.4018/jssci.2009062501.

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A new form of denotational mathematics known as Visual Semantic Algebra (VSA) is presented for abstract visual object and architecture manipulations. A set of cognitive theories for pattern recognition is explored such as cognitive principles of visual perception and basic mechanisms of object and pattern recognition. The cognitive process of pattern recognition is rigorously modeled using VSA and Real-Time Process Algebra (RTPA), which reveals the fundamental mechanisms of natural pattern recognition by the brain. Case studies on VSA in pattern recognition are presented to demonstrate VAS’ expressive power for algebraic manipulations of visual objects. VSA can be applied not only in machinable visual and spatial reasoning, but also in computational intelligence as a powerful man-machine language for representing and manipulating visual objects and patterns. On the basis of VSA, computational intelligent systems such as robots and cognitive computers may process and inference visual and image objects rigorously and efficiently.

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Schweitzer, Dino, Jeff Boleng, Colin Hughes, and Louis Murphy. "Visualizing Keyboard Pattern Passwords." Information Visualization 10, no. 2 (November 18, 2010): 127–33. http://dx.doi.org/10.1057/ivs.2010.12.

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Passwords are fundamental security vulnerabilities in many systems. Several researchers have investigated the trade-off between password memorability versus resiliency to cracking and have looked at alternative systems such as graphical passwords and biometrics. To create stronger passwords, many systems enforce rules regarding the required length and types of characters passwords must contain. Another suggested approach is to use passphrases to combat dictionary attacks. One common ‘trick’ used to remember passwords that conform to complex rules is to select a pattern of keys on the keyboard. Although appearing random, the pattern is easy to remember. The purpose of this research was to investigate how often patterns are used, whether patterns could be classified into common categories, and whether those categories could be used to attack and defeat pattern-based passwords. Visualization techniques were used to collect data and assist in pattern categorization. The approach successfully identified 2 out of 11 passwords in a real-world password file that were not discovered with a traditional dictionary attack. This article will present the approach used to collect and categorize patterns, and describe the resulting attack method that successfully identified passwords in a live system.

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Yamashiro, Susumu, Koichi Nara, and Toichiro Koike. "Transient security enhancement of power systems using pattern recognition." IEEJ Transactions on Power and Energy 105, no. 1 (1985): 7–14. http://dx.doi.org/10.1541/ieejpes1972.105.7.

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Fathi Allam, Diana, Mahmoud Fathi Alalfi, Yasmine Sabry Mahmoud, and Mohammed Atef Abo Ashour. "The Process of Pattern Recognition in the Natural Systems." International Journal of Multidisciplinary Studies in Architecture and Cultural Heritage 1, no. 1 (June 1, 2018): 59–115. http://dx.doi.org/10.21608/ijmsac.2018.181895.

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YAN, Yuling, and Taro SHIMOGO. "State discrimination of vibration systems using pattern recognition technique." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 533 (1991): 106–11. http://dx.doi.org/10.1299/kikaic.57.106.

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