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Shape from Texture without Boundaries

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Abstract

We describe a shape from texture method that constructs an estimate of surface geometry using only the deformation of individual texture elements. Our method does not need to use either the boundary of the observed surface or any assumption about the overall distribution of elements.The method assumes that surface texture elements are drawn from a number of different types, each of fixed shape. Neither the shape of the elements nor the number of types need be known in advance. We show that, with this assumption and assuming a generic, scaled orthographic view and texture, each type of texture element can be reconstructed in a frontal coordinate system from image instances. Interest-point methods supply a method of simultaneously obtaining instances of each texture element automatically and defining each type of element. Furthermore, image instances that have been marked in error can be identified and ignored using the Expectation-Maximization algorithm. A further EM procedure yields a surface reconstruction and a relative irradiance map from the data. We provide numerous examples of reconstructions for images of real scenes, show a comparison between our reconstruction and range maps, and demonstrate that the reconstructions display geometric and irradiance phenomena that can be observed in the original image.

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References

  • Ahuja, N. and Schachter, B. 1983a. Image models. ACM Computing Surveys, 15(1):83–84.

    Article  Google Scholar 

  • Ahuja, N. and Schachter, B. 1983b. Pattern Models. Wiley.

  • Aloimonos, Y. 1986. Detection of surface orientation from texture. i. the case of planes. In IEEE Conf. on Computer Vision and Pattern Recognition, pp. 584–593.

  • Bajcsy, R.K. and Lieberman, L.I. 1976. Texture gradient as a depth cue. Computer Graphics Image Processing, 5(1):52–67.

    Google Scholar 

  • Baraff, D. and Witkin, A. 1998. Large steps in cloth simulation. Computer Graphics, 32(Annual Conference Series):43–54.

    Google Scholar 

  • Berg, T.L., Berg, A.C., Edwards, J., and Forsyth, D.A. 2004. Who's in the picture? In Proc. NIPS.

  • Blake, A. and Marinos, C. 1990. Shape from texture: estimation, isotropy and moments. Artificial Intelligence, 45(3):323–380.

    Article  Google Scholar 

  • Bridson, R., Fedkiw, R., and Anderson, J. 2002. Robust treatment of collisions, contact and friction for cloth animation. Computer Graphics, (Annual Conference Series):594–603.

  • Bridson, R., Marino, S., and Fedkiw, R. 2003. Simulation of clothing with folds and wrinkles. In SCA '03: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer animation, pp. 28–36. Eurographics Association.

  • Choe, Y. and Kashyap, R.L. 1991. 3-D shape from a shaded textural surface image. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(9):907–919.

    Article  Google Scholar 

  • Clerc, M. and Mallat, S. 1999. Shape from texture through deformations. In Int. Conf. on Computer Vision, pp. 405–410.

  • Clerc, M. and Mallat, S. 2002. The texture gradient equation for recovering shape from texture. IEEE T. Pattern Analysis and Machine Intelligence, 24(4):536–549.

    Google Scholar 

  • Daley, D.J. and Vere-Jones, D. 1988. An Introduction to the theory of point processes. Springer-Verlag.

  • Fitzgibbon, A.W. and Zisserman, A. 2002. On affine invariant clustering and automatic cast listing in movies. In Proc. 7th European Conference on Computer Vision. Springer-Verlag.

  • Forsyth, D.A. 2001. Shape from texture and integrability. In Int. Conf. on Computer Vision, pp. 447–452.

  • Forsyth, D.A. 2002. Shape from texture without boundaries. In Proc. ECCV, vol. 3, pp. 225–239.

    MATH  Google Scholar 

  • Forsyth, D.A. and Ponce, J. 2002. Computer Vision: A Modern Approach. Prentice-Hall.

  • Garding, J. 1992. Shape from texture for smooth curved surfaces. In European Conference on Computer Vision, pp. 630–638.

  • Garding, J. 1995. Surface orientation and curvature from differential texture distortion. In Int. Conf. on Computer Vision, pp. 733–739.

  • Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Verlag.

  • Horn, B.K.P. and Schunck, B.G. 1981. Determining optical flow. Artificial Intelligence, 17:185–203.

    Article  Google Scholar 

  • House, D.H., Breen, D., and Breen, D. (Eds.). 2000. Cloth Modelling and Animation. A.K. Peters.

  • Koenderink, J.J. and Van Doorn, A.J. 1983. Geometrical modes as a method to treat diffuse interreflections in radiometry. J. Opt. Soc. Am., 73(6):843–850.

    Google Scholar 

  • Krumm, J. and Shafer, S.A. 1990. Local spatial frequence analysis for computer vision. In Proceedings, Third International Conference on Computer Vision, pp. 354–358.

  • Krumm, J. and Shafer, S.A. 1992. Shape from periodic texture using the spectorgram. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 284–289. IEEE Press.

  • Lazebnik, S., Schmid, C., and Ponce, J. 2003. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In Int. Conf. on Computer Vision.

  • Lazebnik, S., Schmid, C., and Ponce, J. 2003. Sparse texture representation using affine-invariant neighborhoods. In IEEE Conf. on Computer Vision and Pattern Recognition.

  • Lee, K.M. and Kuo, C.C.J. 1998. Direct shape from texture using a parametric surface model and an adaptive filtering technique. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 402–407. IEEE Press.

  • Leung, T. and Malik, J. 1996. Detecting, localizing and grouping repeated scene elements from an image. In European Conference on Computer Vision, pp. 546–555.

  • Lowe, D.G. 1999. Object recognition from local scale-invariant features. In Int. Conf. on Computer Vision, pp. 1150–1157.

  • Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91–110.

    Google Scholar 

  • Lu, R., Koenderink, J., and Kappers, A. 1999. Specularities on surfaces with tangential hairs or grooves. In ICCV, pp. 2–7.

  • Lu, R., Koenderink, J.J., and Kappers, A.M.L. 1998. Optical properties (bidirectional reflection distribution functions) of velvet. Applied Optics, 37(25):5974–5984.

    Google Scholar 

  • Malik, J., Belongie, S., Shi, J. and Leung, T. 1999. Textons, contours and regions: cue integration in image segmentation. In Int. Conf. on Computer Vision, pp. 918–925.

  • Malik, J. and Rosenholtz, R. 1997. Computing local surface orientation and shape from texture for curved surfaces. Int. J. Computer Vision, pp. 149–168.

  • Mikolajczk, K. and Schmid, C. 2003. A performance evaluation of local descriptors. In IEEE Conf. on Computer Vision and Pattern Recognition.

  • Miller, E., Matsakis, N., and Viola, P. 2000. Learning from one example through shared densities on transforms. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 464–471.

    Google Scholar 

  • Mundy, J.L. and Zisserman, A. 1994. Repeated structures: image correspondence constraints and 3d structure recovery. In J.L. Mundy, A. Zisserman, and D.A. Forsyth, (Eds.), Applications of Invariance in Computer Vision, pp. 89–107.

  • Pont, S.C. and Koenderink, J.J. 2002. Bidirectional texture contrast function. In Proc. ECCV: Lecture Notes Comput.Sci 2353, pp. 808–823.

  • Pritchard, D. 2003. Cloth parameters and motion capture. Master's thesis, University of British Columbia.

  • Pritchard, D. and Heidrich, W. 2003. Cloth motion capture. Computer Graphics Forum (Eurographics 2003), 22(3):263–271.

    Google Scholar 

  • Rosenholtz, R. and Malik, J. 1997. Surface orientation from texture: isotropy or homogeneity (or both)? Vision Research, 37(16):2283–2293.

    Article  Google Scholar 

  • Sakai, K. and Finkel, L.H. 1994. A shape-from-texture algorithm based on the human visual psychophysics. In IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, pp. 527–532.

  • Schachter, B. 1980. Model-based texture measures. IEEE Trans. Pattern Analysis and Machine Intelligence, 2(2):169–171.

    Google Scholar 

  • Schachter, B. and Ahuja, N. 1979. Random pattern generation processes. Computer Graphics Image Processing, 10(1):95–114.

    Google Scholar 

  • Schaffalitzky, F. and Zisserman, A. 1999. Geometric grouping of repeated elements within images. In D.A. Forsyth, J.L. Mundy, V. diGesu, and R. Cipolla (Eds.), Shape, Contour and Grouping in Computer Vision, pp. 165–181.

  • Schmid, C. and Mohr, R. 1997. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530–534.

    Article  Google Scholar 

  • Stone, J.V. and Isard, S.D. 1995. Adaptive scale filtering: A general-method for obtaining shape from texture. IEEE Trans. Pattern Analysis and Machine Intelligence, 17(7):713–718.

    Google Scholar 

  • Super, B.J. and Bovik, A.C. 1995. Shape from texture using local spectral moments. IEEE Trans. Pattern Analysis and Machine Intelligence, 17(4):333–343.

    Google Scholar 

  • Terzopolous, D., Platt, J., Barr, A., and Fleischer, K. 1987. Elastically deformable models. In Computer Graphics (SIGGRAPH 87 Proceedings), pp. 205–214.

  • Leung, T. and Malik, J. 1996. Detecting, localizing and grouping repeated scene elements from an image. In European Conference on Computer Vision, pp. 546–555.

  • Miller, T., Berg, A., Edwards, J., Maire, M., White, R., Teh, Y-W., Miller, E., and Forsyth, D.A. 2004. Faces and names in the news. In Proc IEEE Conf. on Computer Vision and Pattern Recognition.

  • Triggs, B. 1998. Autocalibration from planar scenes. In Proc ECCV, vol. 1, pp. 89–105.

    Google Scholar 

  • Witkin, A.P. 1981. Recovering surface shape and orientation from texture. Artificial Intelligence, 17:17–45.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Division, University of California at Berkeley, Berkeley, CA, 94720, USA

    Anthony Lobay & D. A. Forsyth

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  1. Anthony Lobay
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  2. D. A. Forsyth
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Correspondence to Anthony Lobay.

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First online version published in February, 2006

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Lobay, A., Forsyth, D.A. Shape from Texture without Boundaries. Int J Comput Vision 67, 71–91 (2006). https://doi.org/10.1007/s11263-006-4068-8

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  • DOI: https://doi.org/10.1007/s11263-006-4068-8

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