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Готові списки джерел за темами / Invariant pattern recognition

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Добірка наукової літератури з теми "Invariant pattern recognition"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Invariant pattern recognition"

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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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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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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Дисертації з теми "Invariant pattern recognition"

1

Elliffe, Martin C. M. "Neural networks for Invariant pattern recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302530.

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2

Reed, Stuart. "Cascaded linear shift invariant processing in pattern recognition." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/7481.

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Image recognition is the process of classifying a pattern in an image into one of a number of stored classes. It is used in such diverse applications as medical screening, quality control in manufacture and military target recognition. An image recognition system is called shift invariant if a shift of the pattern in the input image produces a proportional shift in the output, meaning that both the class and location of the object in the image are identified. The work presented in this thesis considers a cascade of linear shift invariant optical processors, or correlators, separated by fields of point non-lineari ties, called the cascaded correlator. This is introduced as a method of providing parallel, shiftinvariant, non-linear pattern recognition in a system that can learn in the manner of neural networks. It is shown that if a neural network is constrained to give overall shift invariance, the resulting structure is a cascade of correlators, meaning that the cascaded correlator is the only architecture which will provide fully shift invariant pattern recognition. The issues of training of such a non-linear system are discussed in neural network terms, and the non-linear decisions of the system are investigated. By considering digital simulations of a two-stage system, it is shown that the cascaded correlator is superior to linear filtering for both discrimination and tolerance to image distortion. This is shown for theoretical images and in real-world applications based on fault identification in can manufacture. The cascaded correlator has also been proven as an optical system by implementation in a joint transform correlator architecture. By comparing simulated and optical results, the resulting practical errors are analysed and compensated. It is shown that the optical implementation produces results similar to those of the simulated system, meaning that it is possible to provide a highly non-linear decision using robust parallel optical processing techniques.

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3

Chan, Lai-Wan. "Adaptive and invariant connectionist models for pattern recognition." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238206.

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4

Li, Duwang. "Invariant pattern recognition algorithm using the Hough Transform." PDXScholar, 1989. https://pdxscholar.library.pdx.edu/open_access_etds/3899.

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A new algorithm is proposed which uses the Hough Transform to recognize two dimensional objects independent of their orientations, sizes and locations. The binary image of an object is represented by a set of straight lines. Features of the straight lines, namely the lengths and the angles of their normals, their lengths and the end point positions are extracted using the Hough Transform. A data structure for the extracted lines is constructed so that it is efficient to match the features of the lines of one object to those of another object, and determine if one object is a rotated and/or scaled version of the other. Finally a generalized Hough Transform is used to match the end points of the two sets of lines. The simulation experiments show good results for objects with significant linear features .

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5

Tonge, Ashwini Kishor. "Object Recognition Using Scale-Invariant Chordiogram." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984116/.

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This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the shape and also proven suitable for object detection and digit recognition mechanisms. Additionally, it is translation invariant and robust to shape deformations. In spite of such excellent properties, chordiogram is not scale-invariant. To this end, we propose scale invariant chordiogram descriptors and intend to achieve a similar performance before and after applying scale invariance. Our experiments show that we achieve similar performance with and without scale invariance for silhouettes and real world object images. We also show experiments at different scales to confirm that we obtain scale invariance for chordiogram.

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6

Ojansivu, V. (Ville). "Blur invariant pattern recognition and registration in the Fourier domain." Doctoral thesis, University of Oulu, 2009. http://urn.fi/urn:isbn:9789514292552.

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Abstract Pattern recognition and registration are integral elements of computer vision, which considers image patterns. This thesis presents novel blur, and combined blur and geometric invariant features for pattern recognition and registration related to images. These global or local features are based on the Fourier transform phase, and are invariant or insensitive to image blurring with a centrally symmetric point spread function which can result, for example, from linear motion or out of focus. The global features are based on the even powers of the phase-only discrete Fourier spectrum or bispectrum of an image and are invariant to centrally symmetric blur. These global features are used for object recognition and image registration. The features are extended for geometrical invariances up to similarity transformation: shift invariance is obtained using bispectrum, and rotation-scale invariance using log-polar mapping of bispectrum slices. Affine invariance can be achieved as well using rotated sets of the log-log mapped bispectrum slices. The novel invariants are shown to be more robust to additive noise than the earlier blur, and combined blur and geometric invariants based on image moments. The local features are computed using the short term Fourier transform in local windows around the points of interest. Only the lowest horizontal, vertical, and diagonal frequency coefficients are used, the phase of which is insensitive to centrally symmetric blur. The phases of these four frequency coefficients are quantized and used to form a descriptor code for the local region. When these local descriptors are used for texture classification, they are computed for every pixel, and added up to a histogram which describes the local pattern. There are no earlier textures features which have been claimed to be invariant to blur. The proposed descriptors were superior in the classification of blurred textures compared to a few non-blur invariant state of the art texture classification methods.

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7

Rahtu, E. (Esa). "A multiscale framework for affine invariant pattern recognition and registration." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514286018.

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Abstract This thesis presents a multiscale framework for the construction of affine invariant pattern recognition and registration methods. The idea in the introduced approach is to extend the given pattern to a set of affine covariant versions, each carrying slightly different information, and then to apply known affine invariants to each of them separately. The key part of the framework is the construction of the affine covariant set, and this is done by combining several scaled representations of the original pattern. The advantages compared to previous approaches include the possibility of many variations and the inclusion of spatial information on the patterns in the features. The application of the multiscale framework is demonstrated by constructing several new affine invariant methods using different preprocessing techniques, combination schemes, and final recognition and registration approaches. The techniques introduced are briefly described from the perspective of the multiscale framework, and further treatment and properties are presented in the corresponding original publications. The theoretical discussion is supported by several experiments where the new methods are compared to existing approaches. In this thesis the patterns are assumed to be gray scale images, since this is the main application where affine relations arise. Nevertheless, multiscale methods can also be applied to other kinds of patterns where an affine relation is present. An additional application of one multiscale based technique in convexity measurements is introduced. The method, called multiscale autoconvolution, can be used to build a convexity measure which is a descriptor of object shape. The proposed measure has two special features compared to existing approaches. It can be applied directly to gray scale images approximating binary objects, and it can be easily modified to produce a number of measures. The new measure is shown to be straightforward to evaluate for a given shape, and it performs well in the applications, as demonstrated by the experiments in the original paper.

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8

Woo, Myung Chul. "Biologically-inspired translation, scale, and rotation invariant object recognition models /." Online version of thesis, 2007. http://hdl.handle.net/1850/3933.

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9

Mertzanis, Emmanouel Christopher. "A new neural network based approach to position and scale invariant pattern recognition." Thesis, University of York, 1992. http://etheses.whiterose.ac.uk/10897/.

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10

Heck, Larry Paul. "A subspace approach to the auomatic design of pattern recognition systems for mechanical system monitoring." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15016.

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Книги з теми "Invariant pattern recognition"

1

Object recognition through invariant indexing. Oxford: Oxford University Press, 1995.

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2

Kyrki, Ville. Local and global feature extraction for invariant object recognition. Lappeenranta, Finland: Lappeenranta University of Technology, 2002.

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3

Reiss, Thomas H. Recognizing planar objects using invariant image features. Berlin: Springer-Verlag, 1993.

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4

Tuytelaars, Tinne. Local invariant feature detectors: A survey. Hanover, MA: Now Publishers, 2008.

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5

Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.

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6

Flusser, Jan. Moments and moment invariants in pattern recognition. Chichester, West Sussex, U.K: J. Wiley, 2009.

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7

Flusser, Jan. Moments and moment invariants in pattern recognition. Chichester, West Sussex, U.K: J. Wiley, 2009.

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8

Flusser, Jan. Moments and moment invariants in pattern recognition. Chichester, West Sussex, U.K: J. Wiley, 2009.

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9

D, Downie John, Hine Butler P, and Ames Research Center, eds. Binary optical filters for scale invariant pattern recognition. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1992.

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10

Invariant Object Recognition Based on Elastic Graph Matching (Frontiers in Artificial Intelligence and Applications, 86). Ios Pr Inc, 2002.

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Частини книг з теми "Invariant pattern recognition"

1

Seely, Richard D., Michela Goffredo, John N. Carter, and Mark S. Nixon. "View Invariant Gait Recognition." In Advances in Pattern Recognition, 61–81. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_3.

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2

Mohr, Roger, Patrick Gros, and Cordelia Schmid. "Efficient matching with invariant local descriptors." In Advances in Pattern Recognition, 54–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033226.

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3

Li, Liangqi, Hua Yang, and Lin Chen. "Spatial Invariant Person Search Network." In Pattern Recognition and Computer Vision, 122–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_11.

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4

Deng, Huiqi, Weifu Chen, Andy J. Ma, Qi Shen, Pong C. Yuen, and Guocan Feng. "Robust Shapelets Learning: Transform-Invariant Prototypes." In Pattern Recognition and Computer Vision, 491–502. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03338-5_41.

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5

Rodrigues, João, and J. M. Hans du Buf. "Invariant Multi-scale Object Categorisation and Recognition." In Pattern Recognition and Image Analysis, 459–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72847-4_59.

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6

El Rube, Ibrahim, Maher Ahmed, and Mohamed Kamel. "New Wavelet-Based Invariant Shape Representation Functions." In Pattern Recognition and Image Analysis, 221–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_26.

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7

Ignasiak, Krystian, and Władysław Skarbek. "Pattern Recognition by Invariant Reference Points." In Rough Sets and Current Trends in Computing, 322–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-69115-4_44.

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8

Shen, Weichao, Yuwei Wu, and Yunde Jia. "Temporal Invariant Factor Disentangled Model for Representation Learning." In Pattern Recognition and Computer Vision, 391–402. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_33.

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9

Filipe, Sílvio, and Luís A. Alexandre. "Thermal Infrared Face Segmentation: A New Pose Invariant Method." In Pattern Recognition and Image Analysis, 632–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_75.

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10

Söderberg, Robert, Klas Nordberg, and Gösta Granlund. "An Invariant and Compact Representation for Unrestricted Pose Estimation." In Pattern Recognition and Image Analysis, 3–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_1.

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Тези доповідей конференцій з теми "Invariant pattern recognition"

1

Arsenault, Henri H. "Rotation Invariant Pattern Recognition." In 14th Congress of the International Commission for Optics, edited by Henri H. Arsenault. SPIE, 1987. http://dx.doi.org/10.1117/12.967261.

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2

Mendlovic, D., E. Marom, and N. Konforti. "Scale Invariant Pattern Recognition." In Optical Computing '88, edited by Pierre H. Chavel, Joseph W. Goodman, and Gerard Roblin. SPIE, 1989. http://dx.doi.org/10.1117/12.947906.

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3

Gauselman, Vladimir E., Vadim D. Gleser, Nikolay A. Kaliteevskij, and Victor E. Semenov. "Algorithm of invariant pattern recognition." In Optical Information Processing: International Conference, edited by Yuri V. Gulyaev and Dennis R. Pape. SPIE, 1994. http://dx.doi.org/10.1117/12.166048.

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4

Srestasathiern, Panu, and Alper Yilmaz. "View invariant object recognition." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761238.

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5

Arsenault, H. H. "Advances in optical invariant pattern recognition." In 16th Congress of the International Commission for Optics: Optics as a Key to High Technology. SPIE, 1993. http://dx.doi.org/10.1117/12.2308565.

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6

Martin, T., W. G. Kobel, S. K. Rogers, M. Kabrisky, and J. P. Mills. "A Distortion-Invariant Pattern Recognition Algorithm." In Cambridge Symposium_Intelligent Robotics Systems, edited by David P. Casasent. SPIE, 1987. http://dx.doi.org/10.1117/12.937713.

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Haim, Joseph B., and Joseph Shamir. "Scale- And Rotation-Invariant Pattern Recognition." In 1986 Int'l Computing Conference, edited by Asher A. Friesem, Emanuel Marom, and Joseph Shamir. SPIE, 1987. http://dx.doi.org/10.1117/12.936934.

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8

Vargas, A., Cesar San Martin, Rodolfo Figueroa, Juan Campos, and Maria J. Yzuel. "Invariant pattern recognition with defocused images." In IV Iberoamerican Meeting of Optics and the VII Latin American Meeting of Optics, Lasers and Their Applications, edited by Vera L. Brudny, Silvia A. Ledesma, and Mario C. Marconi. SPIE, 2001. http://dx.doi.org/10.1117/12.437191.

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Hester, Charles F. "Component Spaces For Invariant Pattern Recognition." In 1988 Robotics Conferences, edited by Donald J. Svetkoff. SPIE, 1989. http://dx.doi.org/10.1117/12.949032.

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Arsenault, Henri H., S. Chang, Philippe Gagne, and Oscar Gualdron Gonzalez. "Recent progress in invariant pattern recognition." In Second International Conference on Optical Information Processing, edited by Zhores I. Alferov, Yuri V. Gulyaev, and Dennis R. Pape. SPIE, 1996. http://dx.doi.org/10.1117/12.262620.

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Звіти організацій з теми "Invariant pattern recognition"

1

Li, Duwang. Invariant pattern recognition algorithm using the Hough Transform. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5783.

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2

Kokurina, O. Yu. VIABILITY AND RESILIENCE OF THE MODERN STATE: PATTERNS OF PUBLIC-LEGAL ADMINISTRATION AND REGULATION. Kokurina O.Yu., February 2022. http://dx.doi.org/10.12731/kokurina-21-011-31155.

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Анотація:

The modern understanding of the state as a complex social system allows us to assert that its resilience is based on ensuring systemic homeostasis as a stabilizing dynamic mechanism for resolving contradictions arising in society associated with the threat of losing control over the processes of public administration and legal regulation. Public administration is a kind of social management that ensures the organization of social relations and processes, giving the social system the proper coordination of actions, the necessary orderliness, sustainability and stability. The problem of state resilience is directly related to the resilience of state (public) administration requires a «breakthrough in traditional approaches» and recognition of «the state administration system as an organic system, the constituent parts and elements of which are diverse and capable of continuous self-development». Within the framework of the «organizational point of view» on the control methodology, there are important patterns and features that determine the viability and resilience of public administration and regulation processes in the state and society. These include: W. Ashby's cybernetic law of required diversity: for effective control, the degree of diversity of the governing body must be no less than the degree of diversity of the controlled object; E. Sedov’s law of hierarchical compensations: in complex, hierarchically organized and networked systems, the growth of diversity at the top level in the structure of the system is ensured by a certain limitation of diversity at its lower levels; St. Beer’s principle of invariance of the structure of viable social systems. The study was supported by the RFBR and EISI within the framework of the scientific project No. 21-011-31155.

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