Morphological Modeling of Position-Based Spatial Relationships

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 are sensitive to relative visibility of areas based on partial occlusions, and can also cope with the cases where some objects extend significantly differently relative to others. We show the effectiveness of the proposed definitions using synthetic and real images.


Relative Position-Based Spatial Relationships using Mathematical Morphology
Cinbiş, Ramazan Gökberk (2007-10-19)
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 ...
Dimension reduction using global and local pattern information-based maximum margin criterion
Sakarya, Ufuk (2016-07-01)
Dimension reduction is an important research area in pattern recognition when dealing with high-dimensional data. In this paper, a novel supervised dimension reduction approach is introduced for classification. Advantages of using not only global pattern information but also local pattern information are examined in the maximum margin criterion framework. Experimental comparative results in object recognition, handwritten digit recognition, and hyperspectral image classification are presented. According to ...
3D Extended Object Tracking Using Recursive Gaussian Processes
Kumru, Murat; Özkan, Emre (2018-07-10)
In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can joi...
Dry Dock Detection in Satellite Images with Representation Learning
Aktaş, Ümit Ruşen; Firat, Orhan; Yarman Vural, Fatoş Tunay (2013-04-26)
In this study, we propose a method to detect dry docks, a harbour man-made object which is hard to recognize, using representation learning in satellite images. Dry docks are coastal structures which may include ships for repairing purposes, and they exist in harbour regions. The search space is pruned by making use of two low-level features that invariantly define docks, and remaining samples are used to train a representation learning system. Experimental results suggest that classification methods using ...
Statistical analysis of local 3D structure in 2D images
Kalkan, Sinan; Wörgötter, Florentin; Krüger, Norbert (2006-01-01)
For the analysis of images, a deeper understanding of their intrinsic structure is required. This has been obtained for 2D images by means of statistical analysis [15, 18]. Here, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texture-like structures) and the underlying local 3D structure, represented in terms of continuous surfaces and different kinds of 3D discontinuities, using 3D range data with the true color information. We find that homogeneous ima...
Citation Formats
R. G. Cinbiş, “Morphological Modeling of Position-Based Spatial Relationships,” 2007, Accessed: 00, 2020. [Online]. Available: