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Artigos de revistas sobre o tema "Optical pattern recognition"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 29 de julho de 2024

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1

Solus, Dávid, Ľuboš Ovseník, and Ján Turán. "Microchip Pattern Recognition Based on Optical Correlator." Acta Electrotechnica et Informatica 17, no. 2 (June 1, 2017): 38–42. http://dx.doi.org/10.15546/aeei-2017-0014.

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2

Kumar, Virendra. "Guest Editorial: Optical Pattern Recognition." Optical Engineering 29, no. 9 (1990): 993. http://dx.doi.org/10.1117/12.150767.

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3

Refregier, Ph. "Optical pattern recognition: optimal trade-off circular harmonic filters." Optics Communications 86, no. 2 (November 1991): 113–18. http://dx.doi.org/10.1016/0030-4018(91)90544-n.

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4

Mahlab, Uri, H. John Caulfield, and Joseph Shamir. "Genetic algorithm for optical pattern recognition." Optics Letters 16, no. 9 (May 1, 1991): 648. http://dx.doi.org/10.1364/ol.16.000648.

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5

Tozer, B. "Optical pattern recognition using holographic techniques." Optics & Laser Technology 20, no. 5 (October 1988): 274. http://dx.doi.org/10.1016/0030-3992(88)90032-1.

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6

Mahlab, Uri, Michael Fleisher, and Joseph Shamir. "Error probability in optical pattern recognition." Optics Communications 77, no. 5-6 (July 1990): 415–22. http://dx.doi.org/10.1016/0030-4018(90)90137-i.

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7

Parrish, E. A., A. O. Anyiwo, and T. E. Batchman. "Integrated optical processors in pattern recognition." Pattern Recognition 18, no. 3-4 (January 1985): 227–40. http://dx.doi.org/10.1016/0031-3203(85)90048-2.

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8

Carhart, Gary W., Bret F. Draayer, and Michael K. Giles. "Optical pattern recognition using bayesian classification." Pattern Recognition 27, no. 4 (April 1994): 587–606. http://dx.doi.org/10.1016/0031-3203(94)90039-6.

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9

Chang, Shoude, Philippe Gagné, and Henri H. Arsenault. "Optical Intensity Filters for Pattern Recognition." Journal of Modern Optics 42, no. 10 (October 1995): 2041–50. http://dx.doi.org/10.1080/09500349514551771.

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10

Liu, Hua-Kuang. "Self-amplified optical pattern-recognition technique." Applied Optics 31, no. 14 (May 10, 1992): 2568. http://dx.doi.org/10.1364/ao.31.002568.

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11

Kumar, B. V. K. Vijaya, Z. Bahri, and L. Hassebrook. "Correlation Filters for Optical Pattern Recognition." IETE Journal of Research 35, no. 2 (March 1989): 105–13. http://dx.doi.org/10.1080/03772063.1989.11436800.

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12

Yu, F. T. S., and D. A. Gregory. "Optical pattern recognition: architectures and techniques." Proceedings of the IEEE 84, no. 5 (May 1996): 733–52. http://dx.doi.org/10.1109/5.488743.

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13

Thalmann, R. "Optical pattern recognition using holographic techniques." Optics and Lasers in Engineering 11, no. 3 (January 1989): 217–19. http://dx.doi.org/10.1016/0143-8166(89)90032-8.

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14

Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (May 1, 2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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15

Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (May 1, 2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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16

Zang, Yiming, Yong Qian, Wei Liu, Yongpeng Xu, Gehao Sheng, and Xiuchen Jiang. "A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL." Energies 12, no. 21 (October 28, 2019): 4120. http://dx.doi.org/10.3390/en12214120.

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Optical detection and ultrahigh frequency (UHF) detection are two significant methods of partial discharge (PD) detection in the gas-insulated transmission lines (GIL), however, there is a phenomenon of signals loss when using two types of detections to monitor PD signals of different defects, such as needle defect and free particle defect. This makes the optical and UHF signals not correspond strictly to the actual PD signals, and therefore the characteristic information of optical PD patterns and UHF PD patterns is incomplete which reduces the accuracy of the pattern recognition. Therefore, an image fusion algorithm based on improved non-subsampled contourlet transform (NSCT) is proposed in this study. The optical pattern is fused with the UHF pattern to achieve the complementarity of the two detection methods, avoiding the PD signals loss of different defects. By constructing the experimental platform of optical-UHF integrated detection for GIL, phase-resolved partial discharge (PRPD) patterns of three defects were obtained. After that, the image fusion algorithm based on the local entropy and the phase congruency was used to produce the photoelectric fusion PD pattern. Before the pattern recognition, 28 characteristic parameters are extracted from the photoelectric fusion pattern, and then the dimension of the feature space is reduced to eight by the principal component analysis. Finally, three kinds of classifiers, including the linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), are used for the pattern recognition. The results show that the recognition rate of all the photoelectric fusion pattern under different classifiers is higher than that of optical and UHF patterns, up to the maximum of 95%. Moreover, the photoelectric fusion pattern not only greatly improves the recognition rate of the needle defect and the free particle defect, but the recognition accuracy of the floating defect is also slightly improved.

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17

Xu, Hai Yan, Zhuo Zhang, and Xue Wu Zhang. "Signal Recognition Basing on Optical Fiber Vibration Sensor." Applied Mechanics and Materials 347-350 (August 2013): 743–47. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.743.

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Distributed optical fiber sensor can acquire the information of physical field along time and spatial continuous distribution. It plays an important role in long-distance oil and electricity transmission and security. In this paper, the author introduced the universal steps in triggering pattern recognition, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, three templates are established. And pattern matching had been done between templates and all the samples. The results show that, 87.5 percent of the samples are matched correctly with the triggering patterns they are belonging to.

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18

Tamee, Kreangsak, Khomyuth Chaiwong, Kriengsak Yothapakdee, and Preecha P. Yupapin. "Fringe patterns generated by micro-optical sensors for pattern recognition." Artificial Cells, Nanomedicine, and Biotechnology 43, no. 4 (January 22, 2014): 252–57. http://dx.doi.org/10.3109/21691401.2013.875034.

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19

NAGAE, Sadahiko. "Pattern Recognition by Optical Data Processing (3)." Journal of Graphic Science of Japan 20, no. 2 (1986): 7–13. http://dx.doi.org/10.5989/jsgs.20.2_7.

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20

Patil, Aparna. "Optical Character Recognition Implementation using Pattern Matching." International Journal for Research in Applied Science and Engineering Technology 7, no. 8 (August 31, 2019): 1092–95. http://dx.doi.org/10.22214/ijraset.2019.8155.

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21

Liu, Hua-Kuang. "Bifurcating optical pattern recognition in photorefractive crystals." Optics Letters 18, no. 1 (January 1, 1993): 60. http://dx.doi.org/10.1364/ol.18.000060.

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22

Chang, Shoude. "Invariant optical pattern recognition using calculus descriptors." Optical Engineering 33, no. 12 (December 1, 1994): 4045. http://dx.doi.org/10.1117/12.183407.

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23

Mahlab, Uri, and Joseph Shamir. "Optical pattern recognition based on convex functions." Journal of the Optical Society of America A 8, no. 8 (August 1, 1991): 1233. http://dx.doi.org/10.1364/josaa.8.001233.

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24

Zalman, Gady, and Joseph Shamir. "Reducing error probability in optical pattern recognition." Journal of the Optical Society of America A 8, no. 12 (December 1, 1991): 1866. http://dx.doi.org/10.1364/josaa.8.001866.

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25

Lhamon, Michael E. "Translation‐invariant optical pattern recognition without correlation." Optical Engineering 35, no. 9 (September 1, 1996): 2700. http://dx.doi.org/10.1117/1.600835.

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26

Casasent, David P., and Elizabeth C. Botha. "Knowledge In Optical Symbolic Pattern Recognition Processors." Optical Engineering 26, no. 1 (January 1, 1987): 260134. http://dx.doi.org/10.1117/12.7974018.

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27

Srinivasan, Rajani, Jason Kinser, Marius Schamschula, Joseph Shamir, and H. John Caulfield. "Optical syntactic pattern recognition by fuzzy scoring." Optics Letters 21, no. 11 (June 1, 1996): 815. http://dx.doi.org/10.1364/ol.21.000815.

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28

Kober, V., V. Lashin, I. Moreno, J. Campos, L. P. Yaroslavsky, and M. J. Yzuel. "Color component transformations for optical pattern recognition." Journal of the Optical Society of America A 14, no. 10 (October 1, 1997): 2656. http://dx.doi.org/10.1364/josaa.14.002656.

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29

Toyoda, Haruyoshi. "Pattern recognition system using optical analogue processing." Optics & Laser Technology 29, no. 1 (February 1997): xiii. http://dx.doi.org/10.1016/s0030-3992(97)88163-7.

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30

Rosen, Joseph, Tuvia Kotzer, and Joseph Shamir. "Optical implementation of phase extraction pattern recognition." Optics Communications 83, no. 1-2 (May 1991): 10–14. http://dx.doi.org/10.1016/0030-4018(91)90513-d.

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31

Cheng, Yih-Shyang. "Real-Time Shift-Invariant Optical Pattern Recognition." International Journal of High Speed Electronics and Systems 08, no. 04 (December 1997): 733–48. http://dx.doi.org/10.1142/s0129156497000305.

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Shift invariance is an asset of the VanderLugt correlator, from which the location of the identified object is automatically specified. The development of filters which possess two or three types of invariance (shift, rotation, size, and distortion) simultaneously is reviewed. Various real-time implementation of VanderLugt as well as joint-transform correlators by utilizing spatial light modulators are also reviewed.

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32

Caulfield, H. John, and David Armitage. "Adaptive resonance theory of optical pattern recognition." Applied Optics 28, no. 19 (October 1, 1989): 4060. http://dx.doi.org/10.1364/ao.28.004060.

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33

Metioui, A., and L. Leclerc. "Sidelobe reduction methods in optical pattern recognition." Journal of Optics 21, no. 4 (July 1990): 161–70. http://dx.doi.org/10.1088/0150-536x/21/4/002.

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34

Casasent, D. "General-purpose optical pattern recognition image processors." Proceedings of the IEEE 82, no. 11 (1994): 1724–34. http://dx.doi.org/10.1109/5.333750.

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35

Lejeune, Claude, and Yunlong Sheng. "Optoneural system for invariant pattern recognition." Canadian Journal of Physics 71, no. 9-10 (September 1, 1993): 405–9. http://dx.doi.org/10.1139/p93-063.

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An optoneural system is developed for invariant pattern recognition. The system consists of an optical correlator and a neural network. The correlator uses Fourier–Mellin spatial filters (FMF) for feature extraction. The FMF yields an unique output pattern for an input object. The present method works only with one object present in the input scene. The optical features extracted from the output pattern are shift, scale, and rotation invariant and are used as input to the neural network. The neural network is a multilayer feedforward net with back-propagation learning rule. Because of substantial reduction of the dimension of feature vectors provided by optical FMF, the small neural network is simply simulated in a personal computer. Optical experimental results are shown.

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36

Grunnet-Jepsen, A., S. Tonda, and V. Laude. "Convolution-kernel-based optimal trade-off filters for optical pattern recognition." Applied Optics 35, no. 20 (July 10, 1996): 3874. http://dx.doi.org/10.1364/ao.35.003874.

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37

Wu, Cen, Xuelin Yang, and Weisheng Hu. "Binary Pattern Recognition for High-Speed Optical Signal." Recent Patents on Electrical & Electronic Engineering 6, no. 1 (March 1, 2013): 55–62. http://dx.doi.org/10.2174/2213111611306010007.

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38

Horner, Joseph L. "Optical pattern recognition for validation and security verification." Optical Engineering 33, no. 6 (June 1, 1994): 1752. http://dx.doi.org/10.1117/12.170736.

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39

Goldstein, Dennis H. "Phase-encoding input images for optical pattern recognition." Optical Engineering 33, no. 6 (June 1, 1994): 1806. http://dx.doi.org/10.1117/12.171322.

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40

Ipson, S. S., W. Booth, and K. F. Chang. "Coherent Optical Pattern Recognition Using Computer-Generated Holograms." International Journal of Electrical Engineering Education 28, no. 4 (October 1991): 322–30. http://dx.doi.org/10.1177/002072099102800406.

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41

Davis, Jeffrey A., Don M. Cottrell, Glenn W. Bach, and Roger A. Lilly. "Phase-encoded binary filters for optical pattern recognition." Applied Optics 28, no. 2 (January 15, 1989): 258. http://dx.doi.org/10.1364/ao.28.000258.

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42

Javidi, Bahram. "Guest Editorial: Special Section on Optical Pattern Recognition." Optical Engineering 33, no. 6 (June 1, 1994): 1751. http://dx.doi.org/10.1117/12.181753.

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43

Wen, Zhiqing. "Fuzzy neural network for invariant optical pattern recognition." Optical Engineering 35, no. 8 (August 1, 1996): 2188. http://dx.doi.org/10.1117/1.600825.

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44

Liu, Yue. "Optical pattern recognition by extracting least substructuring elements." Optical Engineering 38, no. 10 (October 1, 1999): 1694. http://dx.doi.org/10.1117/1.602221.

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45

Lee, Sing H. "Optical Implementations Of Digital Algorithms For Pattern Recognition." Optical Engineering 25, no. 1 (January 1, 1986): 250169. http://dx.doi.org/10.1117/12.7973781.

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46

Millán, M. S., J. Romero, M. J. Yzuel, and M. Corbalán. "Optical pattern recognition based on color vision models." Optics Letters 20, no. 16 (August 15, 1995): 1722. http://dx.doi.org/10.1364/ol.20.001722.

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47

Hsu, Magnus T. L., Joachim Knittel, Jean-Francois Morizur, Hans-A. Bachor, and Warwick P. Bowen. "Optical pattern recognition via adaptive spatial homodyne detection." Journal of the Optical Society of America A 27, no. 12 (November 11, 2010): 2583. http://dx.doi.org/10.1364/josaa.27.002583.

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48

Lin, Xin, and Junji Ohtsubo. "Terminal attractor optical associative memory for pattern recognition." Optics & Laser Technology 29, no. 1 (February 1997): xiii. http://dx.doi.org/10.1016/s0030-3992(97)88158-3.

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49

Camp, William O., and Jan Van der Spiegel. "A silicon VLSI optical sensor for pattern recognition." Sensors and Actuators A: Physical 43, no. 1-3 (May 1994): 188–95. http://dx.doi.org/10.1016/0924-4247(93)00692-w.

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50

Lin, Xin, Masahiko Mori, Junji Ohtsubo, and Masanobu Watanabe. "Terminal Attractor Optical Associative Memory for Pattern Recognition." Japanese Journal of Applied Physics 39, Part 1, No. 2B (February 28, 2000): 908–11. http://dx.doi.org/10.1143/jjap.39.908.

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