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By Zhilin Li
With the frequent use of GIS, multi-scale illustration has develop into an immense factor within the realm of spatial information dealing with. targeting geometric variations, this source provides entire insurance of the low-level algorithms to be had for the multi-scale representations of other varieties of spatial positive factors, together with aspect clusters, person strains, a category of traces, person parts, and a category of parts. It additionally discusses algorithms for multi-scale illustration of 3D surfaces and 3D gains. Containing over 250 illustrations to complement the dialogue, the publication presents the newest examine effects, equivalent to raster-based paintings, set of rules advancements, snakes, wavelets, and empirical mode decomposition.
Read or Download Algorithmic Foundation of Multi-Scale Spatial Representation (2006)(en)(280s) PDF
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Extra resources for Algorithmic Foundation of Multi-Scale Spatial Representation (2006)(en)(280s)
Sample text
Li, Z. , A quantitative description model for direction relations based on direction groups, GeoInformatica, 10(2), 177–195, 2006. fm Page 29 Friday, September 8, 2006 11:52 AM 2 Mathematical Background In the algorithms to be presented in the later chapters, mathematical tools at various levels are involved. To facilitate those discussions, this chapter provides some basic mathematical background. 1 COORDINATE SYSTEMS To make a spatial representation possess a certain level of metric quality, a coordinate system needs to be employed.
Scale-driven) generalization: Producing a new line in which the main structure is retained but small details are removed. This operation is dependent on the scales of input and output representations. Partial modification: Modifying the shape of a segment within a line. Point reduction: Reducing the number of points for representation by removing the less important points from a line so that only the so-called critical points are retained. Smoothing: Making the line appear smoother. Typification: Keeping the typical pattern of line bends while removing some.
For example, in 1-D space, the two end points define the boundary of a line. However, this definition is not valid in 2-D space. If one simply adopts the definition in 1-D space to 2-D space, a topological paradox will be caused. To solve this problem, a Voronoi-based spatial algebra for topological relations has been developed by Li et al. 3 for a more detailed discussion) only if necessary. , 2004) for the more detailed differentiation of the disjoint relation. This model makes the use of Voronoi neighbors.