A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

Dormer window, Mt. Nebo House

Arched head leadlight and casement windows with stained glass feature panes and timber muntins. View through window from interior.

Dormer window, Mt. Nebo House

Arched head leadlight window with stained glass feature panes and timber muntins.

Window, Lake Weyba House, Sunshine Coast

Vinyl window to daybed alcove, bedroom pavilion, South-West elevation.

Window, Lake Weyba House, Sunshine Coast

Vinyl window to bath house pavilion, South-West elevation.

Window, Brannigan House, St Lucia, Brisbane

Fitted with external timber blind, South-West elevation.

Door and window detail, Agriculture and Entomology Building, Brisbane

View to side entrance opening and window detail with sliding timber shutters above, from exterior.

Texture alignment in simple shear

We illustrate the flow behaviour of fluids with isotropic and anisotropic microstructure (internal length, layering with bending stiffness) by means of numerical simulations of silo discharge and flow alignment in simple shear. The Cosserat theory is used to provide an internal length in the constitutive model through bending stiffness to describe isotropic microstructure and this theory is coupled to a director theory to add specific orientation of grains to describe anisotropic microstructure. The numerical solution is based on an implicit form of the Material Point Method developed by Moresi et al. [1].

Gene Expression in Brain: A Window on Ethanol Dependence, Neuroadaptation, and Preference

This article represents the proceedings of a symposium at the 2002 joint RSA/ISBRA Conference in San Francisco, California. The organizer was Paula L. Hoffman and the co-chairs were Paula L. Hoffman and Michael Miles. The presentations were (1) Introduction and overview of the use of DNA microarrays, by Michael Miles; (2) DNA microarray analysis of gene expression in brains of P and NP rats, by Howard J. Edenberg; (3) Gene expression patterns in brain regions of AA and ANA rats, by Wolfgang Sommer; (4) Patterns of gene expression in brains of selected lines of mice that differ in ethanol tolerance, by Boris Tabakoff; (5) Gene expression profiling related to initial sensitivity and tolerance in gamma-protein kinase C mutants, by Jeanne Wehner; and (6) Gene expression patterns in human alcoholic brain: from microarrays to protein profiles, by Joanne Lewohl.