Abstract
This paper presents the development of a real-time system for recognition of textured objects. In contrast to current approaches which mostly rely on specialized multiprocessor architectures for fast processing, we use a distributed network architecture to support parallelism and attain real-time performance. In this paper, a new approach to linage matching is proposed as the basis of object localization and positioning, which involves dynamic texture feature extraction and hierarchical image matching. A mask based stochastic method is introduced to extract feature points for matching. Our experimental results demonstrate that the combination of texture feature extraction and interesting point detection provides a better solution to the search of the best matching between two textured images. Furthermore, such an algorithm is implemented on a low cost heterogeneous PVM (Parallel Virtual Machine) network to speed up the processing without specific hardware requirements.
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References
H.G. Barrow, J.M. Tenenbaum, R.C. Bolles and H.C. Wolf, “Parametric correspondence and chamfer matching: Two new techniques for image matching”, Proc. 5th Int. Joint Conf. Artificial Intelligence, Cambridge, MA, pp. 659–663, 1977.
G. Borgefors, “Hierarchical chamfer matching: a parametric edge matching algorithm”, IEEE Trans. Patt. Anal. Machine Intell., Vol. PAMI-10, pp. 849–865, 1988.
D.P. Huttenlocher, G.A. Klanderman and W.J. Rucklidge, “Comparing images using the Hausdorff distance”, IEEE Trans. Patt. Anal. Machine Intell., Vol. PAMI-15, pp. 850–863, 1993.
J. You, E. Pissaloux, J.L. Hellec and P. Bonnin, “A guided image matching approach using Hausdorff distance with interesting points detection” Proc. of 1st IEEE international conference on image processing, Austin, USA, November 13–16, 1994, pp. 968–972.
K.I. Laws, “Textured image segmentation”, Ph.D thesis, University of Southern California, January, 1980.
K.K. Benke, D.R. Skinner and C.J. Woodruff, “Convolution operators as a basis for objective correllates for texture perception”, IEEE Trans. Syst., Man, Cybern., Vol. SMC-18, pp. 158–163, 1988.
R.M. Haralick, “Statistical and structural approaches to texture”, Proc. IEEE, Vol. 67, pp. 786–804, 1979.
T.M. Caelli and D. Reye, “On the classification of image regions by colour, texture and shape”, Pattern Recognition, Vol. 26, No. 4, April, pp. 461–470, 1993.
H.P. Moravec, “Towards automatic visual obstacle avoidance”, Proc. 5th Int. Joint Conf. Artificial Intelligence, Cambridge, MA, pp. 584, 1977.
P. Brodatz, Textures: A Photographic Album for Artists and Designers, Dover, New York, 1966.
T.S. Huang, (ed.), Image Sequence Processing and Dynamic Scene Analysis, Springer-Verlag, New York, 1983.
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© 1995 Springer-Verlag Berlin Heidelberg
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You, J., Zhu, W.P., Cohen, H.A., Pissaloux, E. (1995). Real-time textured object recognition on distributed systems. In: Chin, R.T., Ip, H.H.S., Naiman, A.C., Pong, TC. (eds) Image Analysis Applications and Computer Graphics. ICSC 1995. Lecture Notes in Computer Science, vol 1024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60697-1_92
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DOI: https://doi.org/10.1007/3-540-60697-1_92
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