Ben Marais (1909-1999): The influences on and heritage of a South African Prophet during two periods of transformation

University of Pretoria / Dissertation / Department of Church History and Church Policy / Advised by Prof J W Hofmeyr

Conflict Transformation and the Spiritual Practices for the Military Chaplain

No abstract is available.

The Role of Arts in the Process of Leadership Formation and Ecclesial Transformation

This dissertation investigates how the arts can be instrumental in the process of leadership formation and church transformation. By synthesizing results of an evaluation survey with a holistic-inductive design of inquiry, this dissertation demonstrates that the arts, as expressions of faith, can be transformative. By presenting the arts program at the Old West Church in Boston, which integrates arts as a medium for the expression of the Christian faith, this dissertation provides biblical, theological, and practical frameworks for reflecting on the transformative character of arts in the process of leadership formation and church transformation.

Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.