Nadeem Salamat

On the improvement of combined fuzzy topological and directional relations information

Concept of combined extraction of topological and directional relations information developed by Zahzah et al. [1] by employing the Allens temporal relations in 1D spatial domain was improved by Matsakis and Nikitenko [2]. This latter algorithm has high computational complexity due to its limitations of object approximation and segment fuzzification. In this paper, fuzzy Allen relations are used to define the fuzzy topological and directional relations information between different objects. Some extended results of Salamat and Zahzah [3] are discussed. Polygonal object approximation allows us to use fuzzy operators and this approach reduces computational complexity of the method for computing the combined topological and directional relations. To validate the method, some experiments are tested giving satisfactory and promising results. Affine transformation are depicted, these properties will be helpful for using the method in other areas of image analysis such as object tracking.

Spatial Relations Analysis by Using Fuzzy Operators

Spatial relations play important role in computer vision, scene analysis, geographic information systems (GIS) and content based image retrieval. Analyzing spatial relations by Force histogram was introduced by Miyajima et al [1] and largely developed by Matsakis [2] who used a quantitative representation of relative position between 2D objects. Fuzzy Allen relations are used to define the fuzzy topological relations between different objects and to detect object positions in images. Concept for combined extraction of topological and directional relations by using histogram was developed by J.Malki and E.Zahzah [3], and further improved by Matsakis [4]. This algorithm has high computational and temporal complexity due to its limitations of object approximations. In this paper fuzzy aggregation operators are used for information integration along with polygonal approximation of objects. This approach gives anew, with low temporal and computational complexity of algorithm for the extraction of topological and directional relations.

Fuzzy Spatio-temporal Relations Analysis

There are different families of Spatio-temporal relations such as \emph{same-place same-time}, \emph{same-place different-times}, for road networks like \emph{overtake, derive beside} and many others. These relations describe the relative positions of objects in a spatial scene. In existing techniques, these relations are defined qualitatively. Due to imprecise knowledge information and compensation power to small errors, fuzzy methods are becoming more important. \\In this paper, fuzzy spatio-temporal relations \emph{same-place different-time} and \emph{different-place different time} are introduced. To define these relations, histograms of fuzzy Allen relations and fuzzy dissimilarity measure are used.

Two-dimensional fuzzy spatial relations: a new way of computing and representation

Fuzziness is found everywhere, in modeling spatial relations, fuzziness is found at object level as well as in relation semantics. Commonly, fuzzy topological relations are computed between fuzzy objects. Fuzziness in relation semantics is represented by fuzzy topological relations between crisp objects and these types of fuzzy topological relations are much less developed. In this paper, we propose a method for combining fuzzy topological and directional relations. We also propose an algorithm for defuzzification of relations which provides us a binary topological and directional relation between a 2D object pair. These relations are represented in a neighborhood graph. For validation and assessment, a number of experiments have been performed on artificial data.

Fusion of fuzzy spatial relations

Spatial relations are essential for understanding the image configuration and modeling common sense knowledge In most of existing methods, topological, directional and distance spatial relations are computed separately as they have separate application domains Introduction of Temporal Geographic Information System (TGIS), spatio-temporal reasoning and study of spatio-temporal relations required the computation of topological and metric spatial relations together. In this paper the fuzzy topological and directional relations are integrated with the help of fuzzy Allen relations and directions are evaluated by specific fuzzy membership functions A matrix of fuzzy relations is developed where the topological and directional relations are integrated for a 2D scene Experiments are performed to validate the proposed method The results are analyzed and interpreted from histograms.

Spatio-temporal reasoning by combined topological and directional relations information

Spatio-temporal reasoning is extensively used in many areas of computer vision and artificial intelligence (AI). Topological and directional relations-based reasoning methods are developed separately. Reasoning about moving objects in a spatial scene about the two-dimensional scene simultaneously needs both topological and directional reasoning. In this paper, a reasoning method for two-dimensional spatial scene based on combined topological and directional (CTD) relations method is introduced. Main task in spatial reasoning is the construction of composition tables for topological and directional relations. Entities in these composition-tables follows the mathematical rule for composition of spatial relations, these rules are elaborated and composition table for topological relations is divided and rearranged into sub-tables.

Spatiotemporal Relations and Modeling Motion Classes by Combined Topological and Directional Relations Method

Defining spatiotemporal relations and modeling motion events are emerging issues of current research. Motion events are the subclasses of spatiotemporal relations, where stable and unstable spatio-temporal topological relations and temporal order of occurrence of a primitive event play an important role. In this paper, we proposed a theory of spatio-temporal relations based on topological and orientation perspective. This theory characterized the spatiotemporal relations into different classes according to the application domain and topological stability. This proposes a common sense reasoning and modeling motion events in diverse application with the motion classes as primitives, which describe change in orientation and topological relations model. Orientation information is added to remove the locative symmetry of topological relations from motion events, and these events are defined as a systematic way. This will help to improve the understanding of spatial scenario in spatiotemporal applications.

Combined topological and directional relations based motion event predictions

Spatial changes plays a fundamental role in modeling the spatio-temporal relations and spatio-temporal or motion event predictions. These predictions can be made through the conceptual neighborhood graph using the common sense continuity. This paper investigates that the extension in the temporal interval can effect the whole spatiotemporal relation and motion events. Spatio-temporal predicates form a unit of a motion event. We use the point temporal logic to extend the spatial predicates into the spatio-temporal or motion event predicates.

Fuzzy object retrieval by using histogram of fuzzy Allen relations

Relative position of object description are widely used in event understanding and computer vision tasks especially in object recognition. Use of low level features cannot give satisfactory results when high level concepts is not easily expressible in low level contents. Mostly researchers are concentrating on spatio- temporal relationship between objects or regions of an object in images. Object retrieval which is taken into account the relative position of objects in images become important. In such a case classical Allen relations are used. Searched object can take various shapes and scale according to shooting. Fuzzy methods have the ability to compensate the imprecise informations and vagueness. In this paper fuzzy histograms of Allen relations are used for object retrieval. Fuzzy histograms of Allen relations are the quantitative representation of relative object position. For this purpose Matsakiss [9] algorithm for fuzzification of line segments is refined. This representation is affine invariant. Query is made by example and only corresponding relative relation between objects is considered. Results are analyzed by a well known Receiver Operating Characteristic curve (ROC)method.