2D projection interval relationships: A symbolic representation of spatial relationships

Nabil, Mohammad; Shepherd, John; Ngu, Anne H. H.

doi:10.1007/3-540-60159-7_18

Cited by 47 publications

(29 citation statements)

References 13 publications

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“…-The 2-D projection model (Frank, 1992;Nabil et al, 1995;Safar and Shahabi, 1999) represents spatial relations between objects using MBRs (minimum bounding rectangles). Reasoning between projections of MBRs on the x and y axes is performed using 1-D interval relations.…”

Section: Analysis Of Existing Modelsmentioning

confidence: 99%

“…A number of models for describing and/or computing direction relations have been proposed, including the conebased model (Peuquet and Zhan, 1987;Abdelmoty and Williams, 1994;Shekhar and Liu, 1998), the 2-D projection model (Frank, 1992;Nabil et al, 1995;Safar and Shahabi, 1999), the direction-relation matrix model (Goyal, 2000), and the Voronoi-based model (Yan et al, 2006). These models can compute direction relations between two single objects, but can not compute direction relations between two object groups.…”

Section: Introductionmentioning

confidence: 99%

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An approach to computing direction relations between separated object groups

Yan

Wang

2013

Geosci. Model Dev.

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Abstract. Direction relations between object groups play an important role in qualitative spatial reasoning, spatial computation and spatial recognition. However, none of existing models can be used to compute direction relations between object groups. To fill this gap, an approach to computing direction relations between separated object groups is proposed in this paper, which is theoretically based on gestalt principles and the idea of multi-directions. The approach firstly triangulates the two object groups, and then it constructs the Voronoi diagram between the two groups using the triangular network. After this, the normal of each Voronoi edge is calculated, and the quantitative expression of the direction relations is constructed. Finally, the quantitative direction relations are transformed into qualitative ones. The psychological experiments show that the proposed approach can obtain direction relations both between two single objects and between two object groups, and the results are correct from the point of view of spatial cognition.

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Section: Analysis Of Existing Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An approach to computing direction relations between separated object groups

Yan

Wang

2013

Geosci. Model Dev.

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“…Such applications Can be found in many GIS, for example, a change detection approach for linear features in aerial photographs in [50], digital change detection techniques for civilian and military in [10] and for wide area surveillance in [37]. Specifically, the paper [47] investigated the applicability of image detection techniques for the purpose of emergency response by utilizing remotely sensed bi-temporal imagery data.…”

Section: Change Detection Issue and Related Workmentioning

confidence: 99%

“…' ' ' Minimum Bounding Rectangles (MBRs) are used as the basis for object representation. Tnis approach, as well as that of Nabii [47] Sharma [4SJ and Clementini [49], relies upon the use of MBRs as approximations of the »eometry of spatial objects. The use of MBRs in geographic databases is widely practiced as an efucient way of locating and accessing objects in space [49]' In addition numerous spatial data structures and indexing techniques have been developed that exploit the computationally efficient representation of spatial objects through the use of MBRs [50.…”

Section: Spatial Relationship Modelingmentioning

confidence: 99%

Object-Oriented Distributed Database Design and Spatial Data Modeling

Cobb¹

2002

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Public Reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comment regarding this burden estimates or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215 Work for this grant enabled the advancement of the state of research for areas of fuzzy spatial data modeling, uncertainty and conflation. Specific published work supported by this grant included a range of topics, including: fuzzy spatial relationship refinements, spatial query interfaces, uncertainty in distributed spatial information systems, spatial relationship querying, distributed spatial object-based systems, spatial indexing, distributed conflation model and issues, and an integrated image change detection/conflation algorithm. Special journal issues co-edited by the grant recipient on "Spatial Data Management," "Uncertainty in Geographic Information Systems and Spatial Data" and "Distributed Object-Oriented Systems" resulted in newly published works from some of the foremost researchers in the area, including Michael Goodchild, Peter Fisher, Hans Guesgen, Douglas Schmidt and others.All publications listed are included in this report package, and the reader is referred to the actual publications for details of the topics listed above. IntroductionThis grant was originally issued for the investigation of state-of-the-art technology component-ware for distributed databases utilizing CORBA, as well as the development of a fuzzy spatial data model to provide a way of handling imprecision and uncertainty in spatial data. Specifically, the grant work was to augment ongoing development of NRL's Geospatial Information Database (GIDB) in the realm of Object Request Brokers (ORBs) and the GemStone Object-Oriented Database Management System (OODBMS).Soon after the work was initiated, however, NRL began to investigate alternate implementation architectures for the GIDB relative to its future stability and viability. Major re-design decisions were made, including: (1) a move from the Smalltalk programming language to Java, (2) replacement of GemStone OODBMS with Ozone OODBMS (Java-based), and (3) replacement of CORBA-based architecture with Java Remote Method Invocation (RMI)/applet technology. These decisions have since proven to be invaluable to the development and marketability of the GIDB; however, because this new direction made much of the proposed grant work in CORBA and related implementation issues unnecessary, and in consultation with the NRL principal scientist, the concentration of work was redirected to the areas of fuzzy spatial data modeling and conflation.After the original three years were completed, a no-cost extension was requested. This extension funded a Ph.D. scientific co...

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“…Although several alternatives and refinements have been proposed, a common procedure is to approximate the geometry of spatial objects by Minimum Bounding Rectangles (Nabil et al 1995;Sharma and Flewelling 1995). Many authors, e.g., (Goodchild and Gopal 1990), have stressed the need to handle imprecise and uncertain information about spatial data.…”

Section: Introductionmentioning

confidence: 99%