A noise-aware coding scheme for texture classification

Sensors (Basel). 2011;11(8):8028-44. doi: 10.3390/s110808028. Epub 2011 Aug 15.

Abstract

Texture-based analysis of images is a very common and much discussed issue in the fields of computer vision and image processing. Several methods have already been proposed to codify texture micro-patterns (texlets) in images. Most of these methods perform well when a given image is noise-free, but real world images contain different types of signal-independent as well as signal-dependent noises originated from different sources, even from the camera sensor itself. Hence, it is necessary to differentiate false textures appearing due to the noises, and thus, to achieve a reliable representation of texlets. In this proposal, we define an adaptive noise band (ANB) to approximate the amount of noise contamination around a pixel up to a certain extent. Based on this ANB, we generate reliable codes named noise tolerant ternary pattern (NTTP) to represent the texlets in an image. Extensive experiments on several datasets from renowned texture databases, such as the Outex and the Brodatz database, show that NTTP performs much better than the state-of-the-art methods.

Keywords: adaptive noise band; noise tolerant ternary pattern; texlet.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Databases, Factual
  • Image Interpretation, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / methods
  • Models, Statistical
  • Noise
  • Normal Distribution
  • Pattern Recognition, Automated* / methods
  • Photons
  • Reproducibility of Results
  • Surface Properties