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Journal articles on the topic 'Invariant pattern recognition'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Lenz, Reiner. "Group invariant pattern recognition." Pattern Recognition 23, no. 1-2 (January 1990): 199–217. http://dx.doi.org/10.1016/0031-3203(90)90060-x.

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2

Wood, Jeffrey. "Invariant pattern recognition: A review." Pattern Recognition 29, no. 1 (January 1996): 1–17. http://dx.doi.org/10.1016/0031-3203(95)00069-0.

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3

Kudari, Medha, Shivashankar S., and Prakash S. Hiremath. "Illumination and Rotation Invariant Texture Representation for Face Recognition." International Journal of Computer Vision and Image Processing 10, no. 2 (April 2020): 58–69. http://dx.doi.org/10.4018/ijcvip.2020040105.

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This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.
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4

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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5

Mendlovic, David, Naim Konforti, and Emanuel Marom. "Scale and projection invariant pattern recognition." Applied Optics 28, no. 23 (December 1, 1989): 4982. http://dx.doi.org/10.1364/ao.28.004982.

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6

Chang, Shoude, Henri H. Arsenault, Pascuala Garcia-Martinez, and Chander P. Grover. "Invariant pattern recognition based on centroids." Applied Optics 39, no. 35 (December 10, 2000): 6641. http://dx.doi.org/10.1364/ao.39.006641.

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7

Bharucha, Jamshed J., and W. Einar Mencl. "Two Issues in Auditory Cognition: Self-Organization of Octave Categories and Pitch-Invariant Pattern Recognition." Psychological Science 7, no. 3 (May 1996): 142–49. http://dx.doi.org/10.1111/j.1467-9280.1996.tb00347.x.

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The study of auditory and music cognition provides opportunities to explore general cognitive mechanisms in a specific, highly structured domain We discuss two problems with implications for other domains of perception the self-organization of perceptual categories and invariant pattern recognition The perceptual category we consider is the octave We show how general principles of self-organization operating on a cochlear spectral representation can yield octave categories The example of invariant pattern recognition we consider is the recognition of invariant frequency patterns transformed to different absolute frequencies We suggest a system that uses pitch or musical key to map tones into a pitch-invariant format
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8

Hannagan, Thomas, Frédéric Dandurand, and Jonathan Grainger. "Broken Symmetries in a Location-Invariant Word Recognition Network." Neural Computation 23, no. 1 (January 2011): 251–83. http://dx.doi.org/10.1162/neco_a_00064.

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We studied the feedforward network proposed by Dandurand et al. ( 2010 ), which maps location-specific letter inputs to location-invariant word outputs, probing the hidden layer to determine the nature of the code. Hidden patterns for words were densely distributed, and K-means clustering on single letter patterns produced evidence that the network had formed semi-location-invariant letter representations during training. The possible confound with superseding bigram representations was ruled out, and linear regressions showed that any word pattern was well approximated by a linear combination of its constituent letter patterns. Emulating this code using overlapping holographic representations (Plate, 1995 ) uncovered a surprisingly acute and useful correspondence with the network, stemming from a broken symmetry in the connection weight matrix and related to the group-invariance theorem (Minsky & Papert, 1969 ). These results also explain how the network can reproduce relative and transposition priming effects found in humans.
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9

Rao, G. Mallikarjuna, G. R. Babu, and G. Vijaya Kumari. "Lizard Learning Algorithm for Invariant Pattern Recognition." Journal of Computer Science 3, no. 2 (February 1, 2007): 84–87. http://dx.doi.org/10.3844/jcssp.2007.84.87.

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10

Khachumov, M. V. "INVARIANT MOMENTS AND METRICS IN PATTERN RECOGNITION." Современные наукоемкие технологии (Modern High Technologies), no. 4 2020 (2020): 69–77. http://dx.doi.org/10.17513/snt.37975.

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11

Chen, G. Y., and B. Kégl. "Invariant pattern recognition using contourlets and AdaBoost." Pattern Recognition 43, no. 3 (March 2010): 579–83. http://dx.doi.org/10.1016/j.patcog.2009.08.020.

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12

Hoang, Thai V., and Salvatore Tabbone. "Invariant pattern recognition using the RFM descriptor." Pattern Recognition 45, no. 1 (January 2012): 271–84. http://dx.doi.org/10.1016/j.patcog.2011.06.020.

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13

Chen, Guangyi, and Tien D. Bui. "Invariant Fourier-wavelet descriptor for pattern recognition." Pattern Recognition 32, no. 7 (July 1999): 1083–88. http://dx.doi.org/10.1016/s0031-3203(98)00148-4.

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14

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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15

Rosen, Joseph, and Joseph Shamir. "Distortion invariant pattern recognition with phase filters." Applied Optics 26, no. 12 (June 15, 1987): 2315. http://dx.doi.org/10.1364/ao.26.002315.

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16

Sona, Diego, Alessandro Sperduti, and Antonina Starita. "Discriminant Pattern Recognition Using Transformation-Invariant Neurons." Neural Computation 12, no. 6 (June 1, 2000): 1355–70. http://dx.doi.org/10.1162/089976600300015402.

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To overcome the problem of invariant pattern recognition, Simard, LeCun, and Denker (1993) proposed a successful nearest-neighbor approach based on tangent distance, attaining state-of-the-art accuracy. Since this approach needs great computational and memory effort, Hastie, Simard, and Säckinger (1995) proposed an algorithm (HSS) based on singular value decomposition (SVD), for the generation of nondiscriminant tangent models. In this article we propose a different approach, based on a gradient-descent constructive algorithm, called TD-Neuron, that develops discriminant models. We present as well comparative results of our constructive algorithm versus HSS and learning vector quantization (LVQ) algorithms. Specifically, we tested the HSS algorithm using both the original version based on the two-sided tangent distance and a new version based on the one-sided tangent distance. Empirical results over the NIST-3 database show that the TD-Neuron is superior to both SVD- and LVQ-based algorithms, since it reaches a better trade-off between error and rejection.
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17

Kober, V., Saúl Martínez-Díaz, V. Karnaukhov, and I. A. Ovseyevich. "Distortion-invariant pattern recognition with local correlations." Pattern Recognition and Image Analysis 21, no. 2 (June 2011): 188–91. http://dx.doi.org/10.1134/s1054661811020556.

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18

Yang, Jianwei, Liang Zhang, and Peiyao Li. "Radon–Fourier descriptor for invariant pattern recognition." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (March 2019): 1940004. http://dx.doi.org/10.1142/s0219691319400046.

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Radon transform is not only robust to noise, but also independent on the calculation of pattern centroid. In this paper, Radon–Mellin transform (RMT), which is a combination of Radon transform and Mellin transform, is proposed to extract invariant features. RMT converts any object into a closed curve. Radon–Fourier descriptor (RFD) is derived by applying Fourier descriptor to the obtained closed curve. The obtained RFD is invariant to scaling and rotation. (Generic) R-transform and some other Radon-based methods can be viewed as special cases of the proposed method. Experiments are conducted on some binary images and gray images.
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19

Wood, Jeffrey, and John Shawe-Taylor. "A unifying framework for invariant pattern recognition." Pattern Recognition Letters 17, no. 14 (December 1996): 1415–22. http://dx.doi.org/10.1016/s0167-8655(96)00103-1.

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20

Shen, Lixin. "Noncentral image moments for invariant pattern recognition." Optical Engineering 34, no. 11 (November 1, 1995): 3181. http://dx.doi.org/10.1117/12.213614.

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21

Pintsov, David A. "Invariant pattern recognition, symmetry, and Radon transforms." Journal of the Optical Society of America A 6, no. 10 (October 1, 1989): 1544. http://dx.doi.org/10.1364/josaa.6.001544.

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22

Rahtu, E., M. Salo, and J. Heikkila. "Affine invariant pattern recognition using multiscale autoconvolution." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 6 (June 2005): 908–18. http://dx.doi.org/10.1109/tpami.2005.111.

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23

Selby, Mark, and Roy J. Hughes. "Invariant Spectroscopic Pattern Recognition Using Mellin Transforms." Analytical Chemistry 66, no. 22 (November 15, 1994): 3925–36. http://dx.doi.org/10.1021/ac00094a013.

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24

Chang, Chi-Ching, Yuh-Ping Tong, and Hon-Fai Yau. "Rotational Invariant Pattern Recognition Using Photorefractive Correlator." Japanese Journal of Applied Physics 31, Part 2, No.1A/B (January 15, 1992): L43—L45. http://dx.doi.org/10.1143/jjap.31.l43.

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25

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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26

Verrall, Steven C., and Ramakrishna Kakarala. "Disk-harmonic coefficients for invariant pattern recognition." Journal of the Optical Society of America A 15, no. 2 (February 1, 1998): 389. http://dx.doi.org/10.1364/josaa.15.000389.

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27

Mese, Michihiro, Hiroshi Sako, Takafumi Miyatake, Masakazu Ejiri, and Hirotada Ueda. "Direction-invariant methodology for bill pattern recognition." Systems and Computers in Japan 27, no. 8 (1996): 93–106. http://dx.doi.org/10.1002/scj.4690270808.

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28

Lee, Yim-Kul, and William T. Rhodes. "Invariant pattern recognition using angular signature functions." Applied Optics 32, no. 23 (August 10, 1993): 4372. http://dx.doi.org/10.1364/ao.32.004372.

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29

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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30

Bienenstock, E., and C. von der Malsburg. "A Neural Network for Invariant Pattern Recognition." Europhysics Letters (EPL) 4, no. 1 (July 1, 1987): 121–26. http://dx.doi.org/10.1209/0295-5075/4/1/020.

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31

Wang, Shuenn-Shyang, Po-Cheng Chen, and Wen-Gou Lin. "Invariant pattern recognition by moment fourier descriptor." Pattern Recognition 27, no. 12 (December 1994): 1735–42. http://dx.doi.org/10.1016/0031-3203(94)90090-6.

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32

Cojoc, Dan, M. Teresa Molina, Javier García, and Carlos Ferreira. "Coordinate-transformed filter for shift-invariant and scale-invariant pattern recognition." Applied Optics 36, no. 20 (July 10, 1997): 4812. http://dx.doi.org/10.1364/ao.36.004812.

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33

CHEN, G. Y., and P. BHATTACHARYA. "INVARIANT PATTERN RECOGNITION USING RIDGELET PACKETS AND THE FOURIER TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 02 (March 2009): 215–28. http://dx.doi.org/10.1142/s0219691309002854.

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In this paper, we propose two novel invariant algorithms for pattern recognition by using ridgelet packets and the Fourier transform. Ridgelet packets provide many orthonormal bases that can effectively capture directional features present in pattern images. The Fourier transform is good at eliminating the orientation differences. By combining these two tools, very efficient rotation invariant pattern recognition techniques are created. Experimental results show that the proposed methods achieve very high classification rates and they outperform other state-of-the-art methods for rotation invariant pattern recognition under both noise-free and noisy environments.
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34

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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35

Srinivasa, N., and M. Jouaneh. "A neural network model for invariant pattern recognition." IEEE Transactions on Signal Processing 40, no. 6 (June 1992): 1595–99. http://dx.doi.org/10.1109/78.139270.

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36

Zalevsky, Zeev, David Mendlovic, and Javier García. "Invariant pattern recognition by use of wavelength multiplexing." Applied Optics 36, no. 5 (February 10, 1997): 1059. http://dx.doi.org/10.1364/ao.36.001059.

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37

Ciocoiu, Iulian B. "Invariant pattern recognition using analog recurrent associative memories." Neurocomputing 73, no. 1-3 (December 2009): 119–26. http://dx.doi.org/10.1016/j.neucom.2009.02.024.

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38

Wu, Ronald, and Henry Stark. "Rotation-invariant pattern recognition using optimum feature extraction." Applied Optics 24, no. 2 (January 15, 1985): 179. http://dx.doi.org/10.1364/ao.24.000179.

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39

Arsenault, Henri H., and Claude Delisle. "Contrast-invariant pattern recognition using circular harmonic components." Applied Optics 24, no. 14 (July 15, 1985): 2072. http://dx.doi.org/10.1364/ao.24.002072.

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40

Arsenault, Henri H., and Claude Belisle. "Contrast-invariant pattern recognition using circular harmonic components." Applied Optics 24, no. 19 (October 1, 1985): 3304. http://dx.doi.org/10.1364/ao.24.003304.

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41

Sheng, Yunlong, and Lixin Shen. "Orthogonal Fourier–Mellin moments for invariant pattern recognition." Journal of the Optical Society of America A 11, no. 6 (June 1, 1994): 1748. http://dx.doi.org/10.1364/josaa.11.001748.

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42

Cheng, Yih-Shyang, and Tsair-Chun Liang. "Shift/Rotational-Invariant and Nonuniform Irradiance Pattern Recognition." Japanese Journal of Applied Physics 34, Part 1, No. 1 (January 15, 1995): 169–72. http://dx.doi.org/10.1143/jjap.34.169.

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43

Kasiselvanathan, M., V. Sangeetha, and A. Kalaiselvi. "Palm pattern recognition using scale invariant feature transform." International Journal of Intelligence and Sustainable Computing 1, no. 1 (2020): 44. http://dx.doi.org/10.1504/ijisc.2020.104826.

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44

Lin, Wen-Gou, and Shuenn-Shyang Wang. "A New Neural Model for Invariant Pattern Recognition." Neural Networks 9, no. 5 (July 1996): 899–913. http://dx.doi.org/10.1016/0893-6080(95)00031-3.

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45

Arnoldi, Hans-Martin R., Karl-Hans Englmeier, and Wilfried Brauer. "Translation-invariant pattern recognition based on Synfire chains." Biological Cybernetics 80, no. 6 (June 16, 1999): 433–47. http://dx.doi.org/10.1007/s004220050537.

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46

Pukkila, Tarmo M., and C. Radhakrishna Rao. "Pattern recognition based on scale invariant discriminant functions." Information Sciences 45, no. 3 (August 1988): 379–89. http://dx.doi.org/10.1016/0020-0255(88)90012-6.

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47

Zetzsche, Christoph, and Terry Caelli. "Invariant pattern recognition using multiple filter image representations." Computer Vision, Graphics, and Image Processing 44, no. 3 (December 1988): 369. http://dx.doi.org/10.1016/0734-189x(88)90137-5.

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48

Zetzsche, Christoph, and Terry Caelli. "Invariant pattern recognition using multiple filter image representations." Computer Vision, Graphics, and Image Processing 45, no. 2 (February 1989): 251–62. http://dx.doi.org/10.1016/0734-189x(89)90135-7.

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49

Liu, Dang-Hui, Kin-Man Lam, and Lan-Sun Shen. "Illumination invariant face recognition." Pattern Recognition 38, no. 10 (October 2005): 1705–16. http://dx.doi.org/10.1016/j.patcog.2005.03.009.

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50

Fukumi, M., S. Omatu, F. Takeda, and T. Kosaka. "Rotation-invariant neural pattern recognition system with application to coin recognition." IEEE Transactions on Neural Networks 3, no. 2 (March 1992): 272–79. http://dx.doi.org/10.1109/72.125868.

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