Computing and Handling Cardinal Direction Information

Skiadopoulos, Spiros; Giannoukos, Christos; Vassiliadis, Panos; Sellis, Timos; Koubarakis, Manolis

doi:10.1007/978-3-540-24741-8_20

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…The resulting 25 distinct direction tiles are stored in a 4-value, 5x5 matrix. Skiadopoulos et al [14,13] present an efficient implementation of the direction-relation matrix model that can handle disconnected regions and regions with holes. Chen et al [3] proposed an extension to the direction-relation matrix model which uses the overlay of two grids of directional tiles.…”

Section: Related Workmentioning

confidence: 99%

A splitting line model for directional relations

Buchin

Kusters

Speckmann

et al. 2011

Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Directional relations are fundamental to spatial data queries, analysis and reasoning. Consequently there has been a significant amount of effort to determine directional relations between two regions. However, many existing methods do not perform well when the regions are neighboring or intertwined. In this paper we introduce a new model for directional relations which is based on a splitting line separating the two regions in question. We identify essential quality criteria for directional relation models and translate them into measurable properties of a given splitting line. We present an efficient algorithm that computes an optimal splitting line for two regions and perform extensive experiments. Our results show that the splitting line model captures directional relations very well and that it clearly outperforms existing approaches on pairs of neighboring or intertwined regions.

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Section: Related Workmentioning

confidence: 99%

A splitting line model for directional relations

Buchin

Kusters

Speckmann

et al. 2011

Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…Although much of the image database research has examined matching a single query icon to a database icon, some prior work [1,2,3,4,7,8,9,13,14,16,17,18,23,24] does address queries containing several icons. One approach [3,14,16] involves determining the projections of image objects onto the coordinate axes and then encoding these projections as strings.…”

Section: Introductionmentioning

confidence: 99%

Efficient position-independent iconic search using an R-theta index

Cranston

Samet

2006

Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems

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An iconic image database is a collection of symbolic images where each image is a collection of labeled point features called icons. A method is presented to support fast positionindependent similarity search in an iconic database for symbolic images where the similarity condition involves finding icon pairs that satisfy a specific spatial relationship. This is achieved by introducing an index data structure based on r-θ space, which corresponds to the Cartesian product of separation (i.e., inter-icon distance) and (some representation of) relative spatial orientation. In this space, each pairing of two icons is represented by a single point, and all pairs with the same separation and relative orientation (regardless of absolute position) map to the same point. Similarly, all icon pairs with the same separation but different relative orientations map to points on a line parallel to the θ axis, while all pairs with different separations but the same relative orientation map to points on a line parallel to the r axis. Using such an index, database search for icon pairs with a given spatial relationship or range is accomplished by examining the subarea of the index space into which desired pairs would map. This r-θ index space can be organized using well-known spatial database techniques, such as quadtrees or R-trees. Although the size of such an index grows only linearly with respect to the number of images in the collection, it grows quadratically with the average number of icons in an image. A scheme is described to reduce the size of the index by pruning away a subset of the pairs, at the cost of incurring additional work when searching the database. This pruning is governed by a parameter φ, whose variation provides a continuous range of trade-offs between index size and search time.

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“…The latter algorithm is based on a novel technique for the computation of areas of polygons. To the best of our knowledge, these are the first algorithms that directly handle the aforementioned problem for the cardinal direction relations that can be expressed in [6], [24], [25], [21].…”

Section: Introductionmentioning

confidence: 99%

“…A preliminary version of this paper appears in Proceedings of the of Extending Database Technology Conf. [21].…”

Section: Introductionmentioning

confidence: 99%

Computing and managing cardinal direction relations

Skiadopoulos

Giannoukos

Sarkas

et al. 2005

IEEE Trans. Knowl. Data Eng.

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Abstract-Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent Geographical Information Systems (GIS). Previous research has come up with models to capture cardinal direction relations for typical GIS data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions.

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Computing and Handling Cardinal Direction Information

Cited by 8 publications

References 11 publications

A splitting line model for directional relations

A splitting line model for directional relations

Efficient position-independent iconic search using an R-theta index

Computing and managing cardinal direction relations

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