3D texture recognition using bidirectional feature histograms

Oana G. Cula, Kristin J. Dana

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)33-60
Number of pages28
JournalInternational Journal of Computer Vision
Volume59
Issue number1
DOIs
StatePublished - Aug 1 2004

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • 3D texture
  • Appearance
  • Appearance-based
  • BFH
  • BTF
  • Bidirectional feature histogram
  • Bidirectional texture function
  • Image texton
  • Recognition
  • Texton
  • Texture

Fingerprint Dive into the research topics of '3D texture recognition using bidirectional feature histograms'. Together they form a unique fingerprint.

Cite this