PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Comparison of the Effectiveness of 1D and 2D Hmm in the Pattern Recognition

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Hidden Markov Model (HMM) is a well established technique for image recognition and has also been successfully applied in other domains such as speech recognition, signature verification and gesture recognition. HMM is widely used mechanism for pattern recognition based on 1D data. For images one dimension is not satisfactory, because the conversion of one-dimensional data into a twodimensional lose some information. This paper presents a solution to the problem of 2D data by developing the 2D HMM structure and the necessary algorithms.
Słowa kluczowe
Twórcy
autor
  • Institute of Computer and Information Science, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 73 Dabrowskiego Str., 42-200 Czestochowa, Poland
Bibliografia
  • [1] Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected application in speech recognition. Proceedings of the IEEE, 77(2), 257-285
  • [2] Samaria, F., Young, S. (1994). HMM-based Architecture for Face Identification. Image and Vision Computing, 12(8), 537-583
  • [3] Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of econometrics, 45(1), 39-70
  • [4] Baldi, P., Chau, Y., Hunkapiller, T., McClure, M. A. (1994). Hidden Markov models of biological primary sequence information. Proceedings National Academy of Science, 91, 1059-1063
  • [5] Bobulski, J., Kubanek, M. (2012). Person identification system using sketch of the suspect. Optica Applicata, 4(42), 865-873
  • [6] Valpola, H. (2000). Bayesian Ensemble Learning for Nonlinear Factor Analysis.: Finnish Academies of Technology
  • [7] Eickeler, S., Mller, S., Rigoll, G. (1999). High Performance Face Recognition Using Pseudo 2-D Hidden Markov Models. Paper presented at European Control Conference
  • [8] V Vitoantonio Bevilacqua,L. Cariello, G. Carro, D. Daleno, G. Mastronardi, (2008). A face recognition system based on Pseudo 2D HMM applied to neural network coefficients. Soft Computing, 12(7), 615-621
  • [9] Yujian, L. (2007). An analytic solution for estimating two-dimensional hidden Markov models. Applied Mathematics and Computation, 185(2), 810-822
  • [10] Li, J., Najmi, A., Gray, R. M. (2000). Image classification by a two-dimensional Hidden Markov model. IEEE Transactions on Signal Processing, 48(2), 517-533
  • [11] Joshi, D., Li, J., Wang, J. Z. (2006). A computationally Eficient Approach to the estimation of twoand three-dimensional hidden Markov models. Image Processing, IEEE Transactions on, 15(7), 1871-1886
  • [12] Bobulski, J., Adrjanowicz, L. (2013) Part I. In Artificial Intelligence and Soft Computing (pp. 515-523). : Springer
  • [13] Kanungo, T. (2014). Hidden Markov Model Tutorial. Retrieved from http://www.kanungo.com
  • [14] Forney, G. D. (1973). The Viterbi Algorithm. Procedings of the IEEE, 61(3), 268-278
  • [15] Geusebroek, J. M., Burghouts, G. J., Smeulders, A. W. M. (2005). Amsterdam library of object images. International Journal of Computer Vision, 61(1), 103-112
  • [16] -, (2013). Amsterdam Library of Object Images. Paper presented at http://aloi.science.uva.nl/
  • [17] -, (2014). Database German Traffic Sign Benchmark. Paper presented at http://benchmark.ini.rub.de/Dataset/GTSRB-Final-Training-Images.zip
  • [18] Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C. (2011). The German Traffic Sign Recognition Benchmark: A multi-class classification competition. Paper presented at IEEE International Joint Conference on Neural Networks
  • [19] Nguwi, Y. Y., Cho, S. Y. (2010). Emergent selforganizing feature map for recognizing road sign images. Neural Computing and Application, 19(4), 601-615
  • [20] Hsien, J. C., Liou, Y. S., Chen, S. Y. (2006). Road Sign Detection and Recognition Using Hidden Markov Model. Asian Journal of Health and Information Sciences, 1(1), 85-100
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a0a5ceed-e7b9-4ca2-a2b0-0886601b1226
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.