Abstract
This paper presents a simple approach to the recovery of dense orientation estimates for curved textured surfaces. We make two contributions. Firstly, we show how pairs of spectral peaks can be used to make direct estimates of the slant and tilt angles for local tangent planes to the textured surface. We commence by computing the affine distortion matrices for pairs of corresponding spectral peaks. The key theoretical contribution is to show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local slant and tilt angles. In particular, the leading eigenvector points in the tilt direction. Although not as geometrically transparent, the direction of the second eigenvector can be used to estimate the slant direction. The main practical benefit furnished by our analysis is that it allows us to estimate the orientation angles in closed form without recourse to numerical optimisation. Based on these theoretical properties we present an algorithm for the analysis of regularly textured curved surfaces. We apply the method to a variety of real-world imagery.
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References
J. J. Gibson. The perception of the visual world. Houghton Miffin, Boston, 1950.
David Marr. Vision: A computational investigation into the human representation and processing of visual information. Freeman, 1982.
Andrew Blake and Constantinos Marinos. Shape from texture: estimation, isotropy and moments. Artificial Intelligence, 45(3):323–380, 1990.
John Krumm and Steve A. Shafer. Shape from periodic texture using spectrogram. In IEEE Conference on Computer Vision and Pattern Recognition, pages 284–289, 1992.
B.J. Super and A.C. Bovik. Planar surface orientation from texture spatial frequencies. Pattern Recognition, 28(5):729–743, 1995.
John R. Kender. Shape from texture: an aggregation transform that maps a class of texture into surface orientation. In 6th IJCAI, Tokyo, pages 475–480, 1979.
J.S. Kwon, H.K. Hong, and J.S. Choi. Obtaining a 3-d orientation of projective textures using a morphological method. Pattern Recognition, 29:725–732, 1996.
Jonas Garding. Shape from texture for smooth curved surfaces. In European Conference on Computer Vision, pages 630–638, 1992.
Jonas Garding. Shape from texture for smooth curved surfaces in perspective projection. J. of Mathematical Imaging and Vision, 2:329–352, 1992.
J. Malik and R. Rosenholtz. A differential method for computing local shape-from-texture for planar and curved surfaces. In IEEE Conference on Vision and Pattern Recognition, pages 267–273, 1993.
J. Malik and R. Rosenholtz. Recovering surface curvature and orientation from texture distortion: a least squares algorithm and sensitive analysis. Lectures Notes in Computer Science - ECCV’94, 800:353–364, 1994.
B.J. Super and A.C. Bovik. Shape from texture using local spectral moments. IEEE Trans. on Patt. Anal. and Mach. Intelligence, 17(4):333–343, 1995.
John Krumm and Steve A. Shafer. Texture segmentation and shape in the same image. In IEEE International Conference on Computer Vision, pages 121–127, 1995.
Jonas Garding and Tony Lindeberg. Direct computation of shape cues using scale-adapted spatial derivatives operators. International Journal of Computer Vision, 17(2):163–191, 1994.
P.L. Worthington and E.R. Hancock. New constraints on data-closeness and needle map consistency for shape-from-shading. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(12):1250–1267, December 1999.
J. C. Bezdek. Pattern Recognition with Fuzzy Objective Algorithms. Plenum Press, 1981.
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© 2001 Springer-Verlag Berlin Heidelberg
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Ribeiro, E., Hancock, E.R. (2001). Analysis of Curved Textured Surfaces Using Local Spectral Distortion. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_42
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DOI: https://doi.org/10.1007/3-540-44732-6_42
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