Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : texture


Color Texture Classification Using Adaptive Discrete Multiwavelets Transform

Matheel Emad El-Deen Abdulmunim

Engineering and Technology Journal, 2012, Volume 30, Issue 4, Pages 615-627

The classification of textures images has attracted the attention of many
researchers. The multiscale techniques for gray level texture analysis have been
intensively studied. In this paper, we aim on extending texture classification of color
images by using the multiwavelets transform, a new notion addition to wavelet. The
recognition of textures deals with both feature extraction and classification phases. In
the classification phase the evolutionary computation techniques (genetic
programming) was used for more speed recognition result evaluation. In our
experiment results the proposed method has achieved 99.6% test accuracy on an
average. In addition, the experimental results also show that classification rules
generated by this approach are robust to some noises on textures

Texture Analysis of Brodatz Images Using Statistical Methods

Alyaa Hussain Ali; Alaa Noori Mazher

Engineering and Technology Journal, 2011, Volume 29, Issue 4, Pages 716-724

Textures are one of the important features in computer vision for many
applications. Most of attention has been focused on the texture features. An
important approach to region description is to quantify its texture content.
Although no formal definition of texture exists, intuitively this descriptor provides
measures of properties such as smoothing and regularity. The principal approaches
used in image processing to describe the texture of an image region are statistical,
structural, and spectral. In this paper the features were constructed using different
statistical methods. These are auto-correlation, edge frequency, primitive-length
and law’s method; all these methods were used for texture analysis of Brodatz
images. The result showed that the law’s autocorrelation method yields the best
result.