Relative Position-Based Spatial Relationships using Mathematical Morphology

Spatial information is a crucial aspect of image understanding for modeling context as well as resolving the uncertainties caused by the ambiguities in low-level features. We describe intuitive, flexible and efficient methods for modeling pairwise directional spatial relationships and the ternary between relation using fuzzy mathematical morphology. First, a fuzzy landscape is constructed where each point is assigned a value that quantifies its relative position according to the reference object(s) and the type of the relationship. Then, the degree of satisfaction of this relation by a target object is computed by integrating the corresponding landscape over the support of the target region. Our models support sensitivity to visibility to handle areas that are partially enclosed by objects and are not visible from image points along the direction of interest. They can also cope with the cases where one object is significantly spatially extended relative to others. Experiments using synthetic and real images show that our models produce more intuitive results than other techniques.


Morphological Modeling of Position-Based Spatial Relationships
Cinbiş, Ramazan Gökberk (2007-06-13)
Spatial information plays a very important role in image understanding. Fuzzy mathematical morphology provides an effective basis for extracting binary and ternary spatial relationships by creating a fuzzy landscape where the value at each point corresponds to the relationship degree according to its position with respect to the reference object(s). We improve existing morphological approaches in terms of flexibility and efficiency while also obtaining more intuitive results. Our morphological definitions a...
The enhancement of the cell-based GIS analyses with fuzzy processing capabilities
Yanar, Tahsin Alp; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2003)
In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS software systems to model uncertain real world, the natural variability in the environmental phenomena can not be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used...
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Aygunes, Bulut; Cinbiş, Ramazan Gökberk; Aksoy, Selim (2021-06-01)
Multisource image analysis that leverages complementary spectral, spatial, and structural information benefits fine-grained object recognition that aims to classify an object into one of many similar subcategories. However, for multisource tasks that involve relatively small objects, even the smallest registration errors can introduce high uncertainty in the classification process. We approach this problem from a weakly supervised learning perspective in which the input images correspond to larger neighborh...
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Sozer, A; Yazıcı, Adnan (2002-06-29)
Spatial data are complex and have spatial components and uncertain properties. It is important to develop effective spatial and aspatial indexing techniques to facilitate spatial and/or aspatial querying for databases that deal with spatial data. In this study we discuss a number of spatial index structures, such as Multi-level grid file (MLGF), R-tree, and R*-tree, for fuzzy spatial and/or aspatial queries.
Representation Learning for Contextual Object and Region Detection in Remote Sensing
Firat, Orhan; Can, Gulcan; Yarman Vural, Fatoş Tunay (2014-08-28)
The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is fu...
Citation Formats
R. G. Cinbiş, “Relative Position-Based Spatial Relationships using Mathematical Morphology,” 2007, Accessed: 00, 2020. [Online]. Available: