131 resultados para Iterative closest point algorithm
Resumo:
Detail view of timber cross-bracing to dining studio, as seen from upper living area.
Resumo:
Detail view of timber cross-bracing with polycarbonate sheeting behind as seen from upper level dining studio.
Resumo:
View along circulation deck to belvedere (deck) beyond.
Resumo:
View through courtyard to lower studio dining room, as seen from upper living area.
Resumo:
View through courtyard to lower studio dining room, as seen from upper living area.
Resumo:
North elevation, deck below and belvedere above, as seen from path to beach.
Resumo:
View through courtyard to lower studio dining room, as seen from upper living area.
Resumo:
View of internal cladding to north-east facade as seen from dining studio.
Resumo:
View through courtyard to dining studio as seen from upper living room.
Resumo:
North elevation, deck below and belvedere above, as seen from path to beach.
Resumo:
Detail view of cladding to upper level dining studio.
Resumo:
Recently Adams and Bischof (1994) proposed a novel region growing algorithm for segmenting intensity images. The inputs to the algorithm are the intensity image and a set of seeds - individual points or connected components - that identify the individual regions to be segmented. The algorithm grows these seed regions until all of the image pixels have been assimilated. Unfortunately the algorithm is inherently dependent on the order of pixel processing. This means, for example, that raster order processing and anti-raster order processing do not, in general, lead to the same tessellation. In this paper we propose an improved seeded region growing algorithm that retains the advantages of the Adams and Bischof algorithm fast execution, robust segmentation, and no tuning parameters - but is pixel order independent. (C) 1997 Elsevier Science B.V.