Fieldworks from the Museum of the Void: Experiments in the Event of an Archive

This exhibition was a research presentation of works made at Center for Land Use Interpretation [CLUI]base in Wendover, Utah, USA between 2008-2010. The project was commissioned by the Centre For Land Interpretation in USA and funded by The Henry Moore Foundation in the UK. Documentation of research conducted in the field as made available as video and installation. An experimental discourse on the preservation of land art was put with GPS drawings and research information displayed as maps and documents. In examining physical sites in Utah, USA, the project connected to contemporary discourse centred on archives in relation to land art and land use. Using experimental processes conceived in relation to key concepts such as event structures and entropy, conceptual frameworks developed by Robert Smithson (USA) and John Latham (UK), the 'death drive' of the archive was examined in the context of a cultural impulse to preserve iconic works. The work took items from Lathams archive and placed them at the canonical 'Spiral Jetty', Smithson land art work at Rozel Point north of Salt Lake City. This became a focus for the project that also highlighted the role of the Getty Foundation in documenting major public artworks and CLUI in creating an American Land Museum. Work was created in the field at extreme remote locations using GPS technologies and visual tools were developed to articulate the concepts of the artists discussed, to engage the exhibition audience in ideas of transformation and entropy in art. Audiences were encouraged to sign a petition to be used in future preservation of spiral jetty currently facing development challenges.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.