Authorís Marcin Kociolek,

National Institute of Standards and Technology

Manufacturing Systems Integration Division (826)

Design Processing Group

office: Metrology (220), Room A122

address: 100 Bureau Drive, Stop 8263

            :† Gaithersburg, MD 20899-8263

phone: (+1 301) 975-5994

fax: (+1 301) 975-4482


Mentor: Ram D. Sriram (301) 975-3507

I am not a Sigma Xi member





Although there is no strict definition of the image texture, it is easily perceived by humans and is believed to be a rich source of visual information Ė about the nature and threedimensional shape of physical objects [1]. Generally speaking, textures are complex visual patterns composed of entities, or subpatterns, that have characteristic brightness, colour, slope, size, etc. Thus texture can be regarded as a similarity grouping in an image The local subpattern properties give rise to the perceived lightness, uniformity, density, roughness, regularity, linearity, frequency, phase, directionality, coarseness, randomness, fineness, smoothness, granulation, etc., of the texture as a whole. There are four major issues in texture analysis:

  1. Feature extraction: to compute a characteristic of a digital image able to numerically describe its texture properties;
  2. Texture discrimination: to partition a textured image into regions, each corresponding to a perceptually homogeneous texture (leads to image segmentation);
  3. Texture classification: to determine to which of a finite number of physicallydefined classes (such as normal and abnormal tissue) a homogeneous texture region belongs;
  4. Shape from texture: to reconstruct 3D surface geometry from texture information.

Feature extraction is the first stage of image texture analysis. Results obtained from this stage are used for texture discrimination, texture classification or object shape determination.

This poster presents application of modified 2D discrete wavelet transform derived features for digital image texture classification. During the described research, software for computation of proposed features has been developed. Described texture classification method was tested on Brodatz texture sets. The robustness of the proposed method of texture classification was demonstrated.

This work summarizes Authorís self researches, leaded in years 2004,2005 in the Institute of Electronics, Technical University of Lodz.


[1] A. Materka, M. Strzelecki: Texture Analysis Methods - A Review;