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3D Texture Recognition Using Bidirectional Feature Histograms

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Abstract

Textured surfaces are an inherent constituent of the natural surroundings, therefore efficient real-world applications of computer vision algorithms require precise surface descriptors. Often textured surfaces present not only variations of color or reflectance, but also local height variations. This type of surface is referred to as a 3D texture. As the lighting and viewing conditions are varied, effects such as shadowing, foreshortening and occlusions, give rise to significant changes in texture appearance. Accounting for the variation of texture appearance due to changes in imaging parameters is a key issue in developing accurate 3D texture models. The bidirectional texture function (BTF) is observed image texture as a function of viewing and illumination directions. In this work, we construct a BTF-based surface model which captures the variation of the underlying statistical distribution of local structural image features, as the viewing and illumination conditions are changed. This 3D texture representation is called the bidirectional feature histogram (BFH). Based on the BFH, we design a 3D texture recognition method which employs the BFH as the surface model, and classifies surfaces based on a single novel texture image of unknown imaging parameters. Also, we develop a computational method for quantitatively evaluating the relative significance of texture images within the BTF. The performance of our methods is evaluated by employing over 6200 texture images corresponding to 40 real-world surface samples from the CUReT (Columbia-Utrecht reflectance and texture) database. Our experiments produce excellent classification results, which validate the strong descriptive properties of the BFH as a 3D texture representation.

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References

  • Aksoy, S. and Haralick, R.M. 1999. Graph-theoretic clustering for image grouping and retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 63–68.

    Google Scholar 

  • Bovik, A.C., Clark, M., and Geisler, W.S. 1990. Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):55–73.

    Google Scholar 

  • Chantler, M. 1995. Why illuminant direction is fundamental to texture analysis. IEE Proceedings Vision, Image and Signal Processing, 142(4):199–206.

    Google Scholar 

  • Cula, O.G. and Dana, K.J. 2001a. Compact representation of bidirectional texture functions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. I, pp. 1041–1047.

    Google Scholar 

  • Cula, O.G. and Dana, K.J. 2001b. Recognition methods for 3D textured surfaces. In Proceedings of SPIE Conference on Human Vision and Electronic Imaging VI, vol. 4299, pp. 209–220.

    Google Scholar 

  • Cula, O.G. and Dana, K.J. 2002. Image-based skin analysis. In Proceedings of Texture 2002—The 2nd International Workshop on Texture Analysis and Synthesis, pp. 35–40.

  • Cula, O.G., Dana, K.J., Murphy, F.P., and Rao, B.K. 2004. Skin texture modeling. International Journal of Computer Vision (to appear).

  • Dana, K.J. and Nayar, S.K. 1998. Histogram model for 3D textures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 618–624.

  • Dana, K.J. and Nayar, S.K. 1999a. 3D textured surface modeling. In IEEE Workshop on the Integration of Appearance and Geometric Methods in Object Recognition, pp. 46–56.

  • Dana, K.J. and Nayar, S.K. 1999b. Correlation model for 3D texture. International Conference on Computer Vision, pp. 1061–1067.

  • Dana, K.J., van Ginneken, B., Nayar, S.K., and Koenderink, J.J. 1997. Reflectance and texture of real world surfaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 151–157.

  • Dana, K.J., van Ginneken, B., Nayar, S.K., and Koenderink, J.J. 1999. Reflectance and texture of real world surfaces. ACMTransactions on Graphics, 18(1):1–34.

    Google Scholar 

  • Jain, A., Prabhakar, S., and Hong, L. 1999. A multichannel approach to fingerprint classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):348–369.

    Google Scholar 

  • Julesz, B. 1981. Textons, the elements of texture perception and their interactions. Nature, 290:91–97.

    Google Scholar 

  • Koenderink, J.J., van Doorn, A.J., Dana, K.J., and Nayar, S.K. 1999. Bidirectional reflection distribution function of thoroughly pitted surfaces. International Journal of Computer Vision, 31(2/3):129–144.

    Google Scholar 

  • Leung, T. and Malik, J. 1999. Recognizing surfaces using three-dimensional textons. International Conference on Computer Vision, 2:1010–1017.

    Google Scholar 

  • Leung, T. and Malik, J. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29–44.

    Google Scholar 

  • Liu, X., Yu, Y., and Shum, H. 2001. Synthesizing bidirectional texture functions for real-world surfaces. In Proceedings of SIGGRAPH, pp. 97–106.

  • Ma, W.Y. and Manjunath, B.S. 1996. Texture features and learning similarity. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 425–430.

  • McGunnigle, G. and Chantler, M.J. 2000. Rough surface classification using first order statistics from photometric stereo. Pattern Recognition Letters, 21:593–604.

    Google Scholar 

  • Murase, H. and Nayar, S.K. 1995. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 14(1):5–24.

    Google Scholar 

  • Nene, S.A., Nayar, S.K., and Murase, H. 1994. SLAM: A software library for appearance matching. Technical Report CUCS-019-94 Proceedings of ARPA Image Understanding Workshop.

  • Penirschke, A., Chantler, M., and Petrou, M. 2002. Illuminant rotation invariant classification of 3D surface textures using Lissajous's ellipses. In Proceedings of Texture 2002—The 2nd International Workshop on Texture Analysis and Synthesis, pp. 103–108.

  • Puzicha, J., Hoffman, T., and Buchmann, J. 1999. Histogram clustering for unsupervized image segmentation. International Conference on Computer Vision, 2:602–608.

    Google Scholar 

  • Randen, T. and Husoy, J.H. 1999. Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):291–310.

    Google Scholar 

  • Suen, P. and Healey, G. 1998. Analyzing the bidirectional texture function. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 753–758.

  • Suen, P. and Healey, G. 2000. The analysis and recognition of realworld textures in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(5):491–503.

    Google Scholar 

  • Tong, X., Zhang, J., Liu, L., Wang, X., Guo, B., and Shum, H.-Y. 2002. Synthesis of bidirectional texture functions on arbitrary surfaces. In Proceedings of SIGGRAPH, pp. 665–672.

  • van Ginneken, B., Koenderink, J.J., and Dana, K.J. 1999. Texture histograms as a function of irradiation and viewing direction. International Journal of Computer Vision, 31(2/3):169–184.

    Google Scholar 

  • van Ginneken, B., Stavridi, M., and Koenderink, J.J. 1998. Diffuse and specular reflectance from rough surfaces. Applied Optics, 37:130–139.

    Google Scholar 

  • Varma, M. and Zisserman, A. 2002. Classifying images of materials. In Proceedings of the European Conference on Computer Vision, pp. 255–271.

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Authors and Affiliations

  1. CS Department, Rutgers University, Piscataway, NJ, 08854, USA

    Oana G. Cula

  2. ECE Department, Rutgers University, Piscataway, NJ, 08854, USA

    Kristin J. Dana

Authors
  1. Oana G. Cula
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  2. Kristin J. Dana
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Cula, O.G., Dana, K.J. 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Computer Vision 59, 33–60 (2004). https://doi.org/10.1023/B:VISI.0000020670.05764.55

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