Object localization based on directional information case of 2D vector data

Coros, Stelian; Ni, Jingbo; Matsakis, Pascal

doi:10.1145/1183471.1183499

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The models used for computing spatial direction relationships offer the possibility of aiding ILSOS by enhancing their ability to identify locations and directions on maps when navigating large and unfamiliar terrain. In contrast, spatial direction relationships act as a link between visually perceived data and natural language (Coros et al, 2006;Deng & Li, 2008) in cartography and geographic information systems (Du et al, 2008;Skiadopoulos & Koubarakis, 2004). However, the developers of Wemap overlooked these relationships, and instead focused on center spread and communication (Zhang et al, 2020(Zhang et al, , 2022, symbols (Bai et al, 2021;Daria, 2019), and design (Duan et al, 2019;He et al, 2022;Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Wemap‐based direction model for individuals with limited spatial orientation skills

Wang,

Yan,

Wang

et al. 2024

Transactions in GIS

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Spatial direction relationships act as links between visually perceived data and natural language in cartography and geographic information systems. Although the existing direction models are able to compute spatial direction relationships, their adaptability to unique users of Wemap, such as individuals with limited spatial orientation skills (ILSOS), is minimal. In particular, the ability to identify locations and directions on maps can pose serious challenges for ILSOS. As such, a Wemap‐based direction model was developed in this study to address this deficiency, specifically targeting ILSOS. To address these challenges, a model for calculating spatial direction relationships was constructed. Subsequently, a questionnaire was administered to verify the consistency of the proposed model with the spatial perceptions of ILSOS. The experimental results demonstrate that (1) the proposed model can calculate the spatial direction relationships between a reference object and source target based on absolute reference frames; (2) ILSOS account for a certain proportion of the population and have a superior ability to distinguish spatial over geographic directions; and (3) the calculation results of the proposed model are consistent with the spatial cognition of ILSOS.

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Section: Introductionmentioning

confidence: 99%

Wemap‐based direction model for individuals with limited spatial orientation skills

Wang,

Yan,

Wang

et al. 2024

Transactions in GIS

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“…Within a GIS there are two ways to represent data [15]: a vector layer [4]o ra raster one. Without loss of generality, let us consider spatial data in the rest of the article [i.e., two dimensions Px ; y ðÞ ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Vector Structures for Transient-Phenomenon Representation

Grandchamp¹

2017

Lecture Notes in Geoinformation and Cartography

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This paper deals with data structures within GIS. Continuous phenomena are usually represented by raster structures for simplicity reasons. With such structures, spatial repartitions of the data are not easily interpretable. Moreover, in an overlapping/clustering context, these structures remove the links between the data and the algorithms. We propose a vector representation of such data based on non-regular multi-ring polygons. The structure requires multi-part nested polygons and new set operations. We present the formalism based on belief theory and uncertainty reasoning. We also detail the implementation of the structures and the set operations. The structures and the set operations are illustrated in the context of forest classification having diffuse transitions.

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“…In geographic information systems, directional relations have acted as a link between visually perceived data and natural language [12]. So far, much attention has been paid to the development of mathematical/computational models and identification of (target) objects which have a specified relation with a given reference object [12], [27].…”

mentioning

confidence: 99%

“…So far, much attention has been paid to the development of mathematical/computational models and identification of (target) objects which have a specified relation with a given reference object [12], [27]. In this study, we further confine ourselves to the modeling of directional relations between spatial objects.…”

mentioning

confidence: 99%

A Statistical Model for Directional Relations Between Spatial Objects

Deng

2007

Geoinformatica

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Directional relation, as a kind of spatial constraints, has been recognized as being an important means for spatial query, analysis and reasoning. Directional relation is conventionally concerned with two point objects. However, in spatial query and analysis, there is also a need of directional relations between point and line, point and area, line and line, line and area, and area and area. Therefore, conventional definition of direction needs to be extended to include line and area objects (i.e. the so-called extended objects). Existing models for directional relation of extended objects make use of approximate representations (e.g. minimum bounding rectangles) of the extended objects so as to produce some results with unrealistic impression. In this paper, a statistical model is presented. In this new model, (1) an extended spatial object is decomposed into small components; (2) the directional relation between extended spatial objects is then determined by the directions between these small components which form a distribution; and (3) two measures (i.e. range and median direction) are utilized to describe the statistical property of the distribution. This statistical model is based upon the (extended) spatial objects themselves, instead of their approximate representations. An experimental test has been carried out and the result indicates that the directional relations computed from this model is very close to those perceived by human beings.

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Object localization based on directional information case of 2D vector data

Cited by 4 publications

References 11 publications

Wemap‐based direction model for individuals with limited spatial orientation skills

Wemap‐based direction model for individuals with limited spatial orientation skills

Fuzzy-Vector Structures for Transient-Phenomenon Representation

A Statistical Model for Directional Relations Between Spatial Objects

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