Modeling of evolving textures using granulometries

摘要: This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional methods predict class membership using comparisons with finite set predefined classes and identify the closest class. However, where images arise from evolving over time, estimation time state in continuous evolutionary process is required instead. The PEF does this regression modeling techniques state. It flexible which may be based on any suitable image features. Many textures are well suited morphological analysis uses features derived granulometric image. method illustrated both simulated Boolean processes real corrosion. has particular advantages for training sets containing limited numbers observations, case many world industrial inspection scenarios other can fail or perform badly. [41] G.W. Horgan, Mathematical morphology analysing soil structure European Journal Soil Science, vol. 49, pp. 161–173, 1998. [42] C.A. Reid Glasbey, Biological processing enhancement, Image Processing Analysis, A Practical Approach, R. Baldock J. Graham, eds., Oxford University Press, Oxford, UK, 37–67, 2000. [43] B.B. Hubbard, World According Wavelets: Story Technique Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen T. Lonnestad. An evaluation stochastic models synthesis gray-scale texture, Pattern Recognition Letters, 15, 575–585, 1994. [45] Jain F. Farrokhnia, Unsupervised segmentation Gabor filters, Recognition, 24(12), 1167–1186, 1991. [46] Jossang Feder, fractal characterization rough surfaces, Physica Scripta, T44, 9–14, 1992. [47] Katsaggelos Chun-Jen, Iterative restoration, Handbook Video Processing, A. Bovik, ed., Academic London, 208–209, [48] M. K¨oppen, C.H. Nowack G. R¨osel, Pareto-morphology color processing, Proceedings SCIA99, 11th Scandinavian Conference Analysis 1, Kangerlussuaq, Greenland, 195–202, 1999. [49] S. Krishnamachari Chellappa, Multiresolution Gauss-Markov random field segmentation, IEEE Transactions 6(2), 251–267, 1997. [50] Kurita N. Otsu, Texture by higher order local autocorrelation features, ACCV93, Asian Computer Vision, Osaka, 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos Kouloumbi, Digital system detecting, classifying, fast retrieving corrosion generated defects, Coatings Technology, 73(915), 67–73, 2001. [52] Y. Liu, Zhao Zhang, Learning multispectral cervical cancer detection, 2002 International Symposium Biomedical Imaging: Macro Nano, 169–172, 2002. [53] McGunnigle M.J. Chantler, Modeling deposition surface Electronics 37(12), 749–750, [54] McKenzie, Marshall, A.J. Gray E.R. Dougherty, Morphological function, Artificial Intelligence, 17(2), 167–185, 2003. [55] Classification dynamically functions, Ph.D. Thesis, Strathclyde, 2004. [56] S.G. Mallat, approximations wavelet orthonormal bases L2(R), American Society, 315, 69–87, 1989. [57] theory multiresolution signal decomposition: representation, Machine 11, 674–693, [58] B.S. Manjunath W.Y. Ma, browsing retrieval data, 18, 837–842, 1996. [59] Manjunath, G.M. Haley Multiband 367–381, [60] Matheron, Random Sets Integral Geometry, Wiley Series Probability Statistics, John Sons, New York, 1975.