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- W2344149301 abstract "The importance of spatial information is manifested by the amount of effort spent collecting and maintaining it. Spatial representations are ubiquitous in both natural and artificial systems although the spatial nature of data is often subtle and neglected. A problem exists in that spatial information is sometimes unknown, ambiguous or just plain wrong. The question asked by this research is, “Given a set of signals, how can the spatial arrangement of the sources be automatically inferred, without knowledge of what the signal values are supposed to be?” Pursuing this question has led to the investigation of the phenomenon of spatial redundancy and the development of a computer algorithm, JIGSAW. JIGSAW is a novel method for the recovery of spatial information from a set of signals. It is similar in spirit to Self Organising Maps, but uses ideas from Multidimensional Scaling and Variograms. The developed algorithm embodies efficient methods for the incremental, adaptive construction of a spatial representation for signals. It is scalable and can deal with vast amounts of input data. JIGSAW has been extensively applied to the domain of computer vision where the task is to discover the relative locations of pixels in digital video. A particular application within this domain is the registration or merging of images from multiple video cameras. It has also been applied to the discovery of the relative locations of weather stations based on meteorological variables, and to the calibration of a robotic pan-tilt device to an image space defined by a camera. JIGSAW is designed as a general algorithm for any set of signals or data that exhibit redundancy This work discusses the extendibility of JIGSAW to other sensor types, and to the analysis and presentation of vast amounts of data." @default.
- W2344149301 created "2016-06-24" @default.
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- W2344149301 date "2005-01-01" @default.
- W2344149301 modified "2023-09-27" @default.
- W2344149301 title "Intelligence, redundancy and space" @default.
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