9. Texture and Shape Analysis

  1. Tinku Acharya and
  2. Ajoy K. Ray

Published Online: 20 SEP 2005

DOI: 10.1002/0471745790.ch9

Image Processing: Principles and Applications

Image Processing: Principles and Applications

How to Cite

Acharya, T. and Ray, A. K. (2005) Texture and Shape Analysis, in Image Processing: Principles and Applications, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471745790.ch9

Publication History

  1. Published Online: 20 SEP 2005
  2. Published Print: 19 AUG 2005

ISBN Information

Print ISBN: 9780471719984

Online ISBN: 9780471745792



  • texture;
  • gray level co-occurrence;
  • texture spectrum;
  • fractal dimension;
  • polygonal approximation;
  • shape descriptor;
  • active contour model;
  • gun classification


Textures and shapes reveal important information concerning the fundamental visual elements in an image and characterizes the surfaces of many classes of objects. In view of its multifaceted properties, textures find lot of applications in image processing, such as in automatic surface inspection, remote sensing, medical image analysis, and so on. A well-known statistical tool for extracting second-order texture information from images is the Gray Level Co-occurrence Matrix (GLCM), which has been discussed in this chapter.

Some of the important set of features, which characterizes the images, have been presented here. Other important techniques like texture spectrum, fractal in texture classification have also been presented in this chapter. Like textures, the form or the shape is another fundamental unit of perception. Shape is the ensemble of all the geometrical information of an object which do not change even when the location, scale and orientation of the object are changed. In this chapter, a number of techniques like polygonal approximation, chain code based shape descriptors, and active contour modeling, have been described. An interesting application of shape discrimination using region of support method has been presented here.