參考文獻 |
[1] Beucher, S. The watershed transform applied to image segmentation, Proceedings of the Pfefferkorn Conference on Signal and Image Processing in Microscopy and Microanalysis, pp. 299–314, September 1991.
[2] Vincent, L., and Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transaction on Pattern Analysis and. Machine Intelligence, vol. 13, no. 6, pp. 583–598, June 1991.
[3] Hernandez, S.E., and Barner, K.E. Joint region merging criteria for watershed-based image segmentation, Proceedings of the IEEE International Conference on Image Processing, vol. 2, pp. 108–111, 2000.
[4] Smet, P.D., Luis, R., and Pires, V.P.M. Implementation and analysis of an optimized rainfalling watershed algorithm, in: Proceedings of the Science and Technology Conference, Image and Video Communications and Processing, January 2000.
[5] Roerdink, J. B. T. M., and Meijsterm, A. The Watershed Transform: Denitions, Algorithms and Parallelization Strategies, Fundamenta Informaticae 41, pp. 187-228, 2001.
[6] Digabel, H., and Lantuejoul, C. Iterative algorithms, Actes du Second Symposium Europeen d'Analyse Quantitative des Microstructures en Sciences des Materiaux, Biologie et Medecine, Caen, 4-7 October 1977, J.-L. Chermant, Ed., Riederer Verlag, Stuttgart, pp. 85-99, 1978.
[7] Lantuejoul, C. La squelettisation et son application aux mesures topologiques des mosaiques polycristallines. PhD thesis, Ecole des Mines, Paris, 1978.
[8] Park, J., and Keller, J.M. Snakes on the watershed, IEEE Trans. on Pattern Recognition and Machine Intelligence, vol. 23, no. 10, pp. 1201-1205, October 2001.
[9] Nguyen, H.T., Worring, M., and Boomgaard, R.V.D. Watersnakes: energy-driven watershed segmentation, IEEE Trans. on Pattern Recognition and Machine Intelligence, vol. 25, no. 3, pp. 330-342, March 2003.
[10] Blaffert, T., Dippel, S., Stahl, M., and Wiemker, R. The Laplace integral for a watershed segmentation, Proceedings of 2000 International Conference on Image Processing, vol. 3, pp. 444-447, 2000.
[11] Moga, A. Parallel watershed algorithms for image segmentation, PHD Thesis, Tampere University of Technology, Tampere, Finland, February 1997.
[12] Moga, A.N., and Gabbouj, M. Parallel maker-based image segmentation with watershed transformation, Journal of Parallel and Distributed Computing 51, pp. 27-45, 1998.
[13] Klein, J.C., Lemonnier, F., Gauthier, M., and Peyrard, R. Hardware implementation of the watershed zone algorithm based on a hierarchical queue structure, Proceedings of IEEE Workshop on Nonlinear Signal and Image processing, Neos Marmaras, Halkidiki, Greece, I. Pitas, Ed., pp. 859-862, June 1995.
[14] Noguet, D., Merle, A., and Lattard, D. A data dependent architecture based on seeded region growing strategy for advanced morphological operators, Mathematical Morphology and its Applications to Image and Signal Processing, P. Maragos, R. W. Shafer, and M. A. Butt, Eds. Kluwer Acad. Publ., Dordrecht, pp. 235-243, 1996.
[15] Kuo, C.J., Odeh, S.F., and Huang, M.C. Image segmentation with improved watershed algorithm and its FPGA implementation, Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 753-756, 2001.
[16] Chien, S.-Y., Huang, Y.-W., and Chen L.-G. Predictive watershed: a fast watershed algorithm for video segmentation, IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, issue 5, pp. 453-461, May 2003.
[17] Beare, R. A locally constrained watershed transform, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, issue 7, pp. 1063-1074, July 2006.
[18] Davies, D., Palmer, P., and Mirmehdi, M. Detection and tracking of very small low contrast objects, Proceedings of the British Machine Vision 9th Conference, pp. 599–608, September 1998.
[19] Ffrench, P.A., Zeidler, J.R., and Ku, W.H. Enhanced detectability of small objects in correlated clutter using an improved 2-d adaptive lattice algorithm, IEEE Trans. on Image Process. 3 (6), pp. 383–397, 1997.
[20] Sonka, M., Hlavac, V., and Boyle, R. Image Processing, Analysis, and Machine Vision, 2nd ed., Brooks/Cole Publishing, pp. 77–82, 1999.
[21] Scheaffer, R.L. Introduction to Probability and its Applications, 2nd ed., The Book Company, pp. 285, 1995.
[22] Daubechies, I. Ten Lectures on Wavelets, SIAM, 1992.
[23] Tou, J.T., and Gonzalez, R.C. Pattern Recognition Principles, Addison-Wesley Publishing, 1974.
[24] Dunn, J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster, J. Cybern., vol. 3, no. 3, pp. 32–57, 1973.
[25] Rish, I. An empirical study of the naive Bayes classifier, IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
[26] Fukushima, K. Cognitron: A Self-Organizing Multilayered Neural Network, Biological Cybernetics 20, pp. 121–136, 1975.
[27] Baum, L.E., and Petrie, T. Statistical inference for probabilistic functions of finite state Markov chains, Ann. Math. Stat., vol. 37, pp. 1554-1563, 1966.
[28] Baum, L.E., and Egon, J. A. An inequality with applications to statistical estimation for probabilistic functions of a Markov process and to a model for ecology, Bull. Amer. Meteorol. Soc., vol. 73, pp. 360-363, 1967.
[29] Baum, L.E., and Sell, G. R. Growth functions for transformations on manifolds, Pac. /. Math., vol. 27, no. 2, pp. 211-227, 1968.
[30] Baum, L.E., Petrie, T., Soules, G., and Weiss, N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Stat., vol. 41, no. 1, pp. 164-171, 1970.
[31] Baum, L.E. An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes, Inequalities, vol. 3, pp. 1-8, 1972.
[32] Vapnik, V. Statistical Learning Theory. New York: Wiley, 1998.
[33] Bernhard, S., Christopher, J.C.B., and Alexander, J.S. Advances in Kernel Methods, The MIT Press, 1998.
[34] Hsu, C.-W., and Lin, C.-J. A simple decomposition method for support vector machines, Machine Learning 46, pp. 291-314, 2002.
[35] Hsu, C.-W., and Lin, C.-J. A comparison on methods for multi-class support vector machines, IEEE Transactions on Neural Networks, vol. 13, pp. 415-425, 2002.
[36] Lucas, S.M., and Cho, K.T. Fast convolutional OCR with the scanning N-tuple grid, Proceedings of 8th International Conference on Document Analysis and Recognition, pp. 799-803, August 2005.
[37] Beucher, S., and Lantuejoul, C. Use of watersheds in contour detection, Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, September 1979.
[38] Meyer, F., and Beucher, S. Morphological Segmentation, Journal of Visual Communication and Image Representation, vol. 1, Academic Press, pp. 21-46, September 1990.
[39] Beucher, S., and Meyer, F. The morphological approach to segmentation: the watershed transformation, Mathematical Morphology in Image Processing, E. R. Dougherty, Ed. Marcel Dekker, New York, ch. 12, pp. 433-481, 1993.
[40] Beucher, S. Watershed, hierarchical segmentation and waterfall algorithm, Mathematical Morphology and its Applications to Image Processing, J. Serra and P. Soille, Eds. Kluwer Acad. Publ., Dordrecht, pp. 69-76, 1994.
[41] Meyer, F. Topographic distance and watershed lines, Signal Processing 38, pp. 113-125, 1994.
[42] Gao, H., Siu, W.-C., and Hou, C.-H. Improved techniques for automatic image segmentation, IEEE Trans. on Circuits and Systems for Video Technology, vol. 11, no. 12, pp. 1273-1280, December 2001.
[43] Bieniek, A., and Moga, A. A connected component approach to the watershed segmentation, Mathematical Morphology and its Applications to Image and Signal Processing, H. J. A. M. Heijmans and J. B. T. M. Roerdink, Eds. Kluwer Acad. Publ., Dordrecht, pp. 215-222, 1998.
[44] Lezoray, O., and Cardot, H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images, IEEE Trans. on Image Processing, vol. 11, no. 7, pp. 783-789, July 2002.
[45] Soni, T., Zeidler, J.R., and Ku, W.H. Recursive estimation techniques for detection of small objects in infrared image data, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 581-584, March 1992. |