Integrating a traditional minority into a Nordic society: Elite discourse on the Finnish Roma

Contemporary citizenship studies have been more concerned with the theory and philosophy of citizenship than with empirical studies. The general objective of this contribution is to broaden the understanding of how notions of citizenship are constructed and re-valued in the social world. The study draws on a qualitative analysis of political elite discourse on Romani issues in the Finnish Parliament from 1989-2003. How issues concerning the Roma are debated elucidates the dilemmas of universal rights and duties within the Nordic welfare model, and the possibilities for cultural diversity within this framework. While the Finnish parliamentary debate accentuated tolerance and the acceptance of difference as strengthening factors for Finnish social citizenship, it was not before the new millennium that the political discourse changed to increasingly stress notions of discrimination and structural inequalities in relation to the incapability to provide for a full an inclusive citizenship as regards the Romani minority.

An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video

This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.