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An outline is provided of a research programme at a national mapping agency, that aims to use computer vision tools modelled upon the theories of vision perception to improve the efficiency of the update of topographic maps. Based upon the underlying principles of top-down and bottom-up influences on image interpretation, case studies are presented that illustrate results produced after the application of robust algorithms for extracting low-level `primitive' information from aerial photography. In addition to contributing to research that aims to produce automatic mapping systems, it is intended that algorithms for producing these primitives will form the basis of semi-automated tools that help photogrammetric operators update maps in an increasingly efficient manner. The work presented here is an outline of research programme, upon which the author gratefully acknowledges the contributions of Panja Sai-Ui (2003), Paolo Gamba et al. (2004), Padraig Corcoran and Nick Donnelly.
We describe a semi-automatic cartographic image interpretation system in which techniques previously applied to monochrome images are generalised to colour and extended to support digitization of the full range of multi-coloured cartographic objects. A snapping tool eases the selection of seed points, speeding interpretation. Line and area objects are identified automatically and a suitable target representation (skeleton or contour) produced for each. This simplifies interaction as the user does not have to vary his/her behaviour with object type. Multi-coloured objects can be constructed from their components with ease and correction tools such as snapping and undo both save time and decrease error rates.
A new approach to speckle reduction with texture preservation is presented termed granulometric shrinkage. The technique is analogous to wavelet shrinkage but with the wavelet transform replaced by a nonlinear multiscale granulometric sieve. The sieve employs area morphology, thus removing any shape bias associated with the use of fixed structuring elements. As the granulometric sieve is by nature edge preserving, the benefits of the new approach include improved edge and texture preservation. Experimental results using a SPOT image corrupted by synthetic speckle show the new approach to have a better speckle reduction capability than conventional wavelet shrinkage, with improved preservation of edges and other high frequency image features.
A swarm of unmanned aerial vehicles (UAV) is composed of a group of more than two UAVs that have the same airframe flying together. The platform's goal is to release microvehicles from small surveillance UAV for positive target ID and tagging in urban terrain. In this paper, the author discussed the cooperative localisation and map building of an unknown terrain by a swarm of unmanned aerial vehicles using multiple sensor navigation. It has two primary sources of vehicle position, namely vision sensors and laser sensors. This platform can also be used in ad-hoc networking and video networking. (19 pages)
