Abstract
In this chapter we shall describe the neural classifiers. One of the important tasks in micromechanics for process automation is pattern recognition. For this purpose we developed different neural classifiers. Below, we will describe the Random Threshold Classifier (RTC classifier), Random Subspace Classifier (RSC classifier), and LIRA classifier (LImited Receptive Area). We will describe the structure and functions of these classifiers and how we use them. The first problem is the texture recognition problem. The task of classification in recognition systems is a more important issue than clustering or unsupervised segmentation in a vast majority of applications [1]. Texture classification plays an important role in outdoor scene images recognition, surface visual inspection systems, and so on. Despite its potential importance, there is no formal definition of texture due to an infinite diversity of texture samples. There exists a large number of texture analysis methods in the literature.
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References
Castano R., Manduchi R., Fox J., Classification experiments on real-world texture, Proceedings of the Third Workshop on Empirical Evaluation Methods in Computer Vision, Kauai, Hawaii, 2001, pp. 3–20.
Matti Pietikäinen, Tomi Nurmela, Topi Mäenpää, Markus Turtinen, View-based recognition of real-world textures, Pattern Recognition, Vol.37, 2004, pp. 313–323.
Baidyk T., Comparison of texture recognition with potential functions and neural networks, Neural Networks and Neurocomputers, 1991, pp. 52–61 (in Russian).
Kussul E., Rachkovskij D., Baidyk T., On image texture recognition by associative-projective neurocomputer, Proceedings of the International Conference on Intelligent Engineering Systems through Artificial Neural Networks, 1991, pp. 453–458.
Goltsev A., An assembly neural network for texture segmentation, Neural Networks, Vol. 9, No. 4, 1996, pp. 643–653.
Goltsev A., Wunsch D., Inhibitory connections in the assembly neural network for texture segmentation, Neural Networks, Vol. 11, 1998, pp. 951–962.
Chi-ho Chan, Grantham K.H. Pang, Fabric defect detection by Fourier analysis, IEEE Transactions on Industry Applications, Vol. 36/5, 2000, pp. 1267–1276.
Zschech E., Besser P.. Microstructure characterization of metal interconnects and barrier layers: status and future. Proc. of the IEEE International Interconnect Technology Conference, 2000, pp. 233–235.
Hepplewhite L., Stonham T.J., Surface inspection using texture recognition, Proc. of the 12th IAPR International Conference on Pattern Recognition, Vol. 1, 1994, pp. 589–591.
Patel D., Hannah I., Davies E.R., Foreign object detection via texture analysis, Proc. of the 12th IAPR International Conference on Pattern Recognition, 1994, Vol. 1, pp. 586–588.
Hoogs A., Collins R., Kaicic R., Mundy J., A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25/4, 2003, pp. 458–474.
Leung T., Malik J., Representing and recognizing the visual appearance of materials using three-dimensional textons, Int. J. Comput. Vision, Vol. 43 (1), 2001, pp. 29–44.
Dana K.J., B. van Ginneken, Nayar S.K., Koendrink J.J., Reflectance and texture of real world surfaces, ACM Trans. Graphics, Vol. 18 (1), 1999, pp. 1–34.
Patel D., Stonham T.J., A single layer neural network for texture discrimination, IEEE International Symposium on Circuits and Systems, Vol. 5, 1991, pp. 2656–2660.
Mayorga M.A., Ludeman L.C., Shift and rotation invariant texture recognition with neural nets, Proc. of the IEEE International Conference on Neural Networks, Vol. 6, 1994, pp. 4078–4083.
Woobeom Lee, Wookhyun Kim, Self-organization neural networks for multiple texture image segmentation, Proc. of the IEEE Region 10 Conference TENCON 99, Vol. 1, 1999, pp. 730–733.
Brodatz P., Texture: A Photographic Album for Artists and Designers, Dover Publications, New York, 1966.
Kussul E., Baidyk T., Lukovitch V., Rachkovskij D., Adaptive high performance classifier based on random threshold neurons, Proc. of Twelfth European Meeting on Cybernetics and Systems Research (EMCSR-94), Austria, Vienna, 1994, pp. 1687–1695.
Kussul E., Baidyk T., Kasatkina L., Lukovich V., Rosenblatt Perceptrons for Handwritten Digit Recognition, Proceedings of International Joint Conference on Neural Networks IJCNN, 2001, Vol. 2, 2001, pp. 1516–1520.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., “Gradient-based Learning Applied to Document Recognition”, Proceedings of the IEEE, Vol. 86, No. 11, November 1998, pp. 2278–2344.
Hoque M.S., Fairhurst M.C. “A Moving Window Classifier for Off-line Character Recognition”. Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition, 2000, Amsterdam, pp. 595–600.
S. Belongie, J. Malik, J. Puzicha, Matching shapes, Proceedings of the 8-th IEEE Intern. Conf. on Computer Vision ICCV, Vol. 1, 2001, pp. 454–461.
S. Belongie, J. Malik, J. Puzicha, Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4, 2002, pp. 509–522.
L.Bottou, C. Cortes, J. Denker, H. Drucker, L. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, V. Vapnik, Comparison of classifier methods: a case study in handwritten digit recognition, Proceedings of 12th IAPR Intern. Conf. on Pattern Recognition, 1994, Vol. 2, pp. 77–82.
E. Kussul, T. Baidyk, Improved method of handwritten digit recognition tested on MNIST database, Proceedings of the 15th Intern. Conf. on Vision Interface, Calgary, Canada, 2002, pp. 192–197.
Baidyk T., Kussul E., Makeyev O., Caballero A., Ruiz L., Carrera G., Velasco G. Flat image recognition in the process of microdevice assembly, Pattern Recognition Letters. Vol. 25/1, 2004, pp. 107–118.
Kussul E., Baidyk T., Wunsch D., Makeyev O., Martín A. Permutation coding technique for image recognition systems. IEEE Transactions on Neural Networks, Vol. 17/6, November 2006, pp. 1566–1579.
Cheng-Lin Liu, K. Nakashima, H. Sako, H. Fujisawa, Handwritten digit recognition using state-of-the-art techniques, Proceedings of the 8th Intern. Workshop on Frontiers in Handwritten Recognition, Ontario, Canada, August 2002, pp. 320–325.
Rosenblatt, F., Principles of Neurodynamics, Spartan books, New York, 1962.
Kussul E., Baidyk T., Neural Random Threshold Classifier in OCR Application, Proceedings of the Second All-Ukrainian Intern. Conf. UkrOBRAZ'94, Ukraine, December 1994, pp. 154–157.
Kussul E., Kasatkina L., Rachkovskij D., Wunsch D., Application of Random Threshold Neural Networks for Diagnostics of Micro Machine Tool Condition, International Joint Conference on Neural Networks, NJ Alaska, 1998, Vol. 1, pp. 241–244.
Baidyk T., Kussul E., Makeyev O. Texture Recognition with Random Subspace Neural Classifier, WSEAS Transactions on Circuits and Systems, Issue 4, Vol. 4, April 2005, pp. 319–325.
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Kussul, E., Baidyk, T., Wunsch, D.C. (2010). Neural Classifiers. In: Neural Networks and Micromechanics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02535-8_3
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DOI: https://doi.org/10.1007/978-3-642-02535-8_3
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