Caring at a distance: Gift Theory, aid chains and social movements

This paper takes as its starting point recent work on caring for distant others which is one expression of renewed interest in moral geographies. It examines relationships in aid chains connecting donors/carers in the First World or North and recipients/cared for in the Third World or South. Assuming predominance of relationships between strangers and of universalism as a basis for moral motivation I draw upon Gift Theory in order to characterize two basic forms of gift relationship. The first is purely altruistic, the other fully reciprocal and obligatory within the framework of institutions, values and social forces within specific relationships of politics and power. This conception problematizes donor-recipient relationships in the context of two modernist models of aid chains-the Resource Transfer and the Beyond Aid Paradigms. In the first, donor domination means low levels of reciprocity despite rhetoric about partnership and participation. The second identifies potential for greater reciprocity on the basis of combination between social movements and non-governmental organizations at both national and trans-national levels, although at the risk of marginalizing competencies of states. Finally, I evaluate post-structural critiques which also problematize aid chain relationships. They do so both in terms of bases-such as universals and difference-upon which it might be constructed and the means-such as forms of positionality and mutuality-by which it might be achieved.

A comparison between feature-based and EM-based contour tracking

Most active-contour methods are based either on maximizing the image contrast under the contour or on minimizing the sum of squared distances between contour and image 'features'. The Marginalized Likelihood Ratio (MLR) contour model uses a contrast-based measure of goodness-of-fit for the contour and thus falls into the first class. The point of departure from previous models consists in marginalizing this contrast measure over unmodelled shape variations. The MLR model naturally leads to the EM Contour algorithm, in which pose optimization is carried out by iterated least-squares, as in feature-based contour methods. The difference with respect to other feature-based algorithms is that the EM Contour algorithm minimizes squared distances from Bayes least-squares (marginalized) estimates of contour locations, rather than from 'strongest features' in the neighborhood of the contour. Within the framework of the MLR model, alternatives to the EM algorithm can also be derived: one of these alternatives is the empirical-information method. Tracking experiments demonstrate the robustness of pose estimates given by the MLR model, and support the theoretical expectation that the EM Contour algorithm is more robust than either feature-based methods or the empirical-information method. (c) 2005 Elsevier B.V. All rights reserved.