Abstract
We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their responses to a set of filters. However, this approach brings with it many open questions, including how to combine texture and intensity information into a common descriptor and how to deal with the fact that filter responses inside textured regions are generally spatially inhomogeneous. Our goal in this paper is to introduce two new ideas which address these open questions and to demonstrate the application of these ideas to the segmentation of natural images. The first idea consists of a novel means of describing points in natural images and an associated distance function for comparing these descriptors. This distance function is aided in textured regions by the use of the second idea, a new process introduced here which we have termed area completion. Experimental segmentation results which incorporate our proposed approach into the Normalized Cut framework of Shi and Malik are provided for a variety of natural images.
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References
T.D. Alter. The Role of Saliency and Error Propagation in Visual Object Recognition. PhD Thesis, MIT, 1995.
D.H. Ballard. Cortical connections and parallel processing: Structure and function. The Behavioral and Brain Sciences, 9:67–120, 1986.
J. Bigün and J.M. Hans du Buf. N-folded symmetries by complex moments in gabor space and their application to unsupervised texture segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 16(1):80–87, 1994.
A. Bovik, M. Clark, and W.S. Geisler. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Mach. Intell., 12(1):55–73, 1990.
M. Carandini and D.J. Heeger. Summation and division by neurons in primate visual cortex. Science, 264:1333–1336, 1994.
S. Casadei, S. Mitter, and P. Perona. Boundary detection in piecewise homogeneous images. In Proc. 2nd Europ. Conf. Comput. Vision, G. Sandini (Ed.), LNCS-Series Vol. 588, Springer-Verlag, pages 174–183, 1992.
J.W. Demmel. Applied Numerical Linear Algebra. SIAM, 1997.
R. DeValois and K. DeValois. Spatial Vision. Oxford University Press, 1988.
J.H. Elder and S.W. Zucker. Computing Contour Closure. Fourth European Conf. Computer Vision, 1996, Cambridge, England.
J.N. Franklin. Matrix Theory. Prentice-Hall, 1968.
W. Freeman and E. Adelson. The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell., 13:891–906, 1991.
H. Greenspan, S. Belongie, P. Perona, and R. Goodman. Rotation-Invariant Texture Recognition Using a Steerable Pyramid. 12th Int. Conf. Patt. Rec., 1994.
D. J. Heeger and J.R. Bergen. Pyramid-based texture analysis/synthesis. Computer Graphics: SIGGRAPH, pages 229–238, 1995.
D.W. Jacobs. Finding Salient Convex Groups. In I.J. Cox, P. Hansen, and B Julesz, eds., Partitioning Data Sets, vol. 19 of DIMACS (Series in Discrete Mathematics and Theoretical Computer Science). 1995.
A.K. Jain and F. Farrokhnia. Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12):1167–1186, 1991.
D. Jones and J. Malik. Computational framework for determining stereo correspondence from a set of linear spatial filters. Image and Vision Computing, 10(10), 1992.
H. Knutsson and G.H. Granlund. Texture analysis using two-dimensional quadrature filters. In Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pages 206–213. IEEE Computer Society, 1983.
J.J. Koenderink. Operational significance of receptive field assemblies. Biol. Cybern., 58:163–171, 1988.
J.J. Koenderink and A.J. van Doorn. Representation of local geometry in the visual system. Biol. Cybern., 55:367–375, 1987.
T. Lindeberg. Scale-Space Theory in Computer Vision. Kluwer, 1994.
J. Malik and P. Perona. Preattentive texture discrimination with early vision mechanisms. J. Opt Soc. Am. A, 7(5):923–932, 1990.
B.S. Manjunath and W.Y. Ma. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell., 18(8):837–842, 1996.
J. Puzicha, T. Hofmann, and J.M. Buhmann. Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 267–272, 1997.
F. Reif. Statistical Physics. McGraw-Hill, 1965.
J. Shi and J. Malik. Normalized cuts and image segmentation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 731–737, 1997.
M.R. Turner. Texture discrimination by gabor functions. Biol. Cybern., 55:71–82, 1986.
M. Wertheimer. Laws of organization in perceptual forms(partial translation). In W.B. Ellis, editor, A Sourcebook of Gestalt Psycychology, pages 71–88. Harcourt, Brace and Company, 1938.
S.C. Zhu, Y. Wu, and D. Mumford. Frame: Filters, random fields, and minimax entropy. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 686–693, 1996.
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Belongie, S., Malik, J. (1998). Finding boundaries in natural images: A new method using point descriptors and area completion. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV'98. ECCV 1998. Lecture Notes in Computer Science, vol 1406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055702
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DOI: https://doi.org/10.1007/BFb0055702
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