Abstract
We address the problem of scale selection in texture analysis. Two different scale parameters, feature scale and statistical scale, are defined. Statistical scale is the size of the regions used to compute averages. We define the class of homogeneous random functions as a model of texture. A dishomogeneity function is defined and we prove that it has useful asymptotic properties in the limit of infinite statistical scale. We describe an algorithm for image partitioning which has performed well on piecewise homogeneous synthetic images. This algorithm is embedded in a redundant pyramid and does not require any ad-hoc information. It selects the optimal statistical scale at each location in the image.
Research supported by Airforce grant AFOSR-89-0276-C and ARO grant DAAL03-86-K-0171, Center for Intelligent Control Systems.
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References
H. Knuttson and G. H. Granlund. Texture analysis using two-dimensional quadrature filters. In Workshop on Computer Architecture for Pattern Analysis ans Image Database Management, pages 206–213. IEEE Computer Society, 1983.
M.R. Turner. Texture discrimination by gabor functions. Biol. Cybern., 55:71–82, 1986.
J. Malik and P. Perona. Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America — A, 7(5):923–932, 1990.
A.C. Bovik, M. Clark, and W.S. Geisler. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Machine Intell., 12(1):55–73, 1990.
B. Julesz. Visual pattern discrimination. IRE Transactions on Information Theory IT-8, pages 84–92, 1962.
R. L. Kashyap and K. Eom. Texture boundary detection based on the long correlation model. IEEE transactions on Pattern Analysis and Machine Intelligence, 11:58–67, 1989.
D. Geman, S. Geman, C. Graffigne, and P. Dong. Boundary detection by constraint optimization. IEEE Trans. Pattern Anal. Machine Intell., 12(7):609, 1990.
R. Wilson and G.H. Granlund. The uncertainty principle in image processing. IEEE Trans. Pattern Anal. Machine Intell, 6(6):758–767, Nov. 1984.
M. Spann and R. Wilson. A quad-tree approach to image segmentation which combines statistical and spatial information. Pattern Recogn., 18:257–269, 1985.
S. Casadei. Multiscale image segmentation by dishomogeneity evaluation and local optimization (master thesis). Master's thesis, MIT, Cambridge, MA, May 1991.
S. Casadei, S. Mitter, and P. Perona. Boundary detection in piecewise homogeneous textured images (to appear). Technical Report-, MIT, Cambridge, MA,--.
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© 1992 Springer-Verlag Berlin Heidelberg
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Casadei, S., Mitter, S., Perona, P. (1992). Boundary detection in piecewise homogeneous textured images. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_20
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DOI: https://doi.org/10.1007/3-540-55426-2_20
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