The Industrial Context of Occupational Mobility: Change in Structure

The neglect of a consideration of history has been a feature of mobility research. ‘History’ affects the results of analyses of social mobility by altering the occupational/industrial structure and by encouraging exchange mobility. Changes in industrial structure are rooted more directly in historical causes and can be seen as more fundamental than changes in occupational structure. Following a substantial review of the secondary literature on changes in industrial and occupational structure in Northern Ireland, loglinear analyses of intra- and intergenerational mobility tables for sociologically-derived cohort generations that incorporate occupational and industrial categories are presented. Structural and inheritance effects for industry are as significant as those for occupation. Given the well-established finding of ‘constant social fludity’ in mobility tables once structural effects are controlled, the inclusion of categorization by industry is necessary in order to reach an accurate understanding of occupational mobility and the role of historical change in mobility.

Northern Ireland:The Development Context

This article provides a contextual framework for the new agenda for development, represented in the economic strategy known as Strategy 2010, and the regional spatial plan known as Shaping Our Future. These are considered in the following two articles. This article begins by setting a perspective on the political economy of Northern Ireland and follows with an outline of the spatial planning process. In conclusion, it raises the key challenges facing attempts to renew the region.

Thresholding of noisy shoeprint images based on pixel context

In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.