Bidirectional texture function

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Bidirectional texture function (BTF) [1][2][3] is a 6-dimensional function depending on planar texture coordinates (x,y) as well as on view and illumination spherical angles. In practice this function is obtained as a set of several thousand color images of material sample taken during different camera and light positions.

The BTF is a representation the appearance of texture as a function of viewing and illumination direction. It is an image-based representation, since the geometry of the surface is unknown and not measured. BTF is typically captured by imaging the surface at a sampling of the hemisphere of possible viewing and illumination directions. BTF measurements are collections of images. The term BTF was first introduced in [1][2] and similar terms have since been introduced including BSSRDF [4] and SBRDF (spatial BRDF). SBRDF has a very similar definition to BTF, i.e. BTF is also a spatially varying BRDF.

To cope with a massive BTF data with high redundancy, many compression methods were proposed.[3][5]

Application of the BTF is in photorealistic material rendering of objects in virtual reality systems and for visual scene analysis,[6] e.g., recognition of complex real-world materials using bidirectional feature histograms or 3D textons.

Biomedical and biometric applications of the BTF include recognition of skin texture [7]

See also[edit]

References[edit]

  1. ^ a b Kristin J. Dana; Bram van Ginneken; Shree K. Nayar; Jan J Koenderink (1999). "Reflectance and texture of real world surfaces". ACM Transactions on Graphics, vol. 18, No. 1. pp. 1–34. 
  2. ^ a b Kristin J. Dana; Bram van Ginneken; Shree K. Nayar; Jan J Koenderink (1996). "Reflectance and texture of real world surfaces". Columbia University Technical Report CUCS-048-96. 
  3. ^ a b Jiří Filip; Michal Haindl (2009). "Bidirectional Texture Function Modeling: A State of the Art Survey". IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11 31 (11). pp. 1921–1940. doi:10.1109/TPAMI.2008.246. 
  4. ^ Jensen, H.W.; Marschner, S.R.; Levoy, M.; Hanrahan, P. (2001). "A practical model for subsurface light transport". ACM SIGGRAPH. pp. 511–518. 
  5. ^ Vlastimil Havran; Jiří Filip; Karol Myszkowski (2009). "Bidirectional Texture Function Compression based on Multi-Level Vector Quantization". Computer Graphics Forum, vol. 29, no. 1. pp. 175–190. 
  6. ^ Michal Haindl,Jiří Filip (2013). "Visual Texture: Accurate Material Appearance Measurement, Representation and Modeling". Advances in Computer Vision and Pattern Recognition, © Springer-Verlag London 2013, (285 p., ISBN 978-1-4471-4901-9). p. 285. 
  7. ^ Oana G. Cula; Kristin J. Dana, Frank P. Murphy and Babar K. Rao (2005). "Skin Texture Modeling". International Journal of Computer Vision. pp. 97–119.