Workshop on Texture Analysis 1998 : Error Detection in Textures using Invariant grey scale Features 1 Error Detection in Textures using Invariant grey scale FeaturesMarc

Automatic Detection of Errors on Textures Using Invariant Grey Scale Features and Polynomial Classifiers

Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions

We propose a new method of texture analysis and classification based on a local center-symmetric covariance analysis, using Kullback (log-likelihood) discrimination of sample and prototype distributions. Features of our analysis are generalized, invariant, local measures of texture having centersymmetric patterns, which is characteristic of many natural and artificial textures. We introduce two...

Texture Classification based on Texton Patterns using on various Grey to Greylevel Preprocessing Methods

Textural patterns can often be used to recognize familiar objects in an image or retrieve images with similar texture from a database. Texture patterns can provide significant and abundance of texture and shape information. One of the recent significant and important texture features called Texton represents the various patterns of image which is useful in texture analysis. However sometimes th...

Invariant Texture Classification Using Group Averaging with Relational Kernel Functions

In this paper we propose a novel method for the construction of textural features which are invariant with respect to 2D Euclidean motion and strictly increasing grey scale transformations. Our approach is based on a group averaging technique with relational kernel functions. In order to allow for comparison the evaluation of our approach was done on two image data sets taken from the Brodatz a...

A Statistical Approach to Local Evaluation of a Single Texture Image

The concept of texture implicitly suggests some local shift invariant statistical properties. Motivated by this idea we have shown in a series of papers that textures can be modelled by estimating the joint probability density of grey levels in a suitably chosen observation window. In this paper we apply the estimated density to evaluate the original texture image locally. At each position of t...