Results 1  10
of
21
Qualitative Spatial Representation and Reasoning
 An Overview”, Fundamenta Informaticae
, 2001
"... The need for spatial representations and spatial reasoning is ubiquitous in AI – from robot planning and navigation, to interpreting visual inputs, to understanding natural language – in all these cases the need to represent and reason about spatial aspects of the world is of key importance. Related ..."
Abstract

Cited by 71 (10 self)
 Add to MetaCart
(Show Context)
The need for spatial representations and spatial reasoning is ubiquitous in AI – from robot planning and navigation, to interpreting visual inputs, to understanding natural language – in all these cases the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research, such as geographic information science
Combining RCC8 with Qualitative Direction Calculi: Algorithms and Complexity ∗
"... Increasing the expressiveness of qualitative spatial calculi is an essential step towards meeting the requirements of applications. This can be achieved by combining existing calculi in a way that we can express spatial information using relations from both calculi. The great challenge is to develop ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
Increasing the expressiveness of qualitative spatial calculi is an essential step towards meeting the requirements of applications. This can be achieved by combining existing calculi in a way that we can express spatial information using relations from both calculi. The great challenge is to develop reasoning algorithms that are correct and complete when reasoning over the combined information. Previous work has mainly studied cases where the interaction between the combined calculi was small, or where one of the two calculi was very simple. In this paper we tackle the important combination of topological and directional information for extended spatial objects. We combine some of the best known calculi in qualitative spatial reasoning (QSR), the RCC8 algebra for representing topological information, and the Rectangle Algebra (RA) and the Cardinal Direction Calculus (CDC) for directional information. Although CDC is more expressive than RA, reasoning with CDC is of the same order as reasoning with RA. We show that reasoning with basic RCC8 and basic RA relations is in P, but reasoning with basic RCC8 and basic CDC relations is NPComplete. 1
Qualitative Spatial Representation and Reasoning in the SparQToolbox
"... Abstract. A multitude of calculi for qualitative spatial reasoning (QSR) have been proposed during the last two decades. The number of practical applications that make use of QSR techniques is, however, comparatively small. One reason for this may be seen in the difficulty for people from outside th ..."
Abstract

Cited by 10 (3 self)
 Add to MetaCart
(Show Context)
Abstract. A multitude of calculi for qualitative spatial reasoning (QSR) have been proposed during the last two decades. The number of practical applications that make use of QSR techniques is, however, comparatively small. One reason for this may be seen in the difficulty for people from outside the field to incorporate the required reasoning techniques into their software. Sometimes, proposed calculi are only partially specified and implementations are rarely available. With the SparQ toolbox presented in this text, we seek to improve this situation by making common calculi and standard reasoning techniques accessible in a way that allows for easy integration into applications. We hope to turn this into a community effort and encourage researchers to incorporate their calculi into SparQ. This text is intended to present SparQ to potential users and contributors and to provide an overview on its features and utilization. 1
Thinking Inside the Box: A Comprehensive Spatial Representation for Video Analysis
"... Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different ki ..."
Abstract

Cited by 8 (6 self)
 Add to MetaCart
(Show Context)
Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.
Regionbased Theories of Space: Mereotopology and Beyond (PhD Qualifying Exam Report, 2009)
"... The very nature of topology and its close relation to how humans perceive space and time make mereotopology an indispensable part of any comprehensive framework for qualitative spatial and temporal reasoning (QSTR). Within QSTR, it has by far the longest history, dating back to descriptions of pheno ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
The very nature of topology and its close relation to how humans perceive space and time make mereotopology an indispensable part of any comprehensive framework for qualitative spatial and temporal reasoning (QSTR). Within QSTR, it has by far the longest history, dating back to descriptions of phenomenological processes in nature (Husserl, 1913; Whitehead, 1920, 1929) – what we call today ‘commonsensical ’ in Artificial Intelligence. There have been plenty of other motivations to
Reasoning about cardinal directions between extended objects
 Artif. Intell
"... Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, known as Cardinal Direction Calculus (CDC), for representing direction relations between connected plane regions. CDC is perhaps the most expressive qualit ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, known as Cardinal Direction Calculus (CDC), for representing direction relations between connected plane regions. CDC is perhaps the most expressive qualitative calculus for directional information, and has attracted increasing interest from areas such as artificial intelligence, geographical information science, and image retrieval. Given a network of CDC constraints, the consistency problem is deciding if the network is realizable by connected regions in the real plane. This paper provides a cubic algorithm for checking consistency of basic CDC constraint networks, and proves that reasoning with CDC is in general an NPComplete problem. For a consistent network of basic CDC constraints, our algorithm also returns a ‘canonical ’ solution in cubic time. This cubic algorithm is also adapted to cope with cardinal directions between possibly disconnected regions, in which case currently the best algorithm is of time complexity O(n 5). 1
Representation and reasoning about general solid rectangles
 In Proceedings of the 23rd International Joint Conference on Artificial Intelligence
"... Entities in twodimensional space are often approximated using rectangles that are parallel to the two axes that define the space, socalled minimumbounding rectangles (MBRs). MBRs are popular in Computer Vision and other areas as they are easy to obtain and easy to represent. In the area of Quali ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
Entities in twodimensional space are often approximated using rectangles that are parallel to the two axes that define the space, socalled minimumbounding rectangles (MBRs). MBRs are popular in Computer Vision and other areas as they are easy to obtain and easy to represent. In the area of Qualitative Spatial Reasoning, many different spatial representations are based on MBRs. Surprisingly, there has been no such representation proposed for general rectangles, i.e., rectangles that can have any angle, nor for general solid rectangles (GSRs) that cannot penetrate each other. GSRs are often used in computer graphics and computer games, such as Angry Birds, where they form the building blocks of more complicated structures. In order to represent and reason about these structures, we need a spatial representation that allows us to use GSRs as the basic spatial entities. In this paper we develop and analyze a qualitative spatial representation for GSRs. We apply our representation and the corresponding reasoning methods to solve a very interesting practical problem: Assuming we want to detect GSRs in computer games, but computer vision can only detect MBRs. How can we infer the GSRs from the given MBRs? We evaluate our solution and test its usefulness in a real gaming scenario. 1
Customizing Qualitative Spatial and Temporal Calculi
"... Abstract. Qualitative spatial and temporal calculi are usually formulated on a particular level of granularity and with a particular domain of spatial or temporal entities. If the granularity or the domain of an existing calculus doesn’t match the requirements of an application, it is either possibl ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Qualitative spatial and temporal calculi are usually formulated on a particular level of granularity and with a particular domain of spatial or temporal entities. If the granularity or the domain of an existing calculus doesn’t match the requirements of an application, it is either possible to express all information using the given calculus or to customize the calculus. In this paper we distinguish the possible ways of customizing a spatial and temporal calculus and analyze when and how computational properties can be inherited from the original calculus. We present different algorithms for customizing calculi and proof techniques for analyzing their computational properties. We demonstrate our algorithms and techniques on the Interval Algebra for which we obtain some interesting results and observations. We close our paper with results from an empirical analysis which shows that customizing a calculus can lead to a considerably better reasoning performance than using the noncustomized calculus. 1
On Combinations of Binary Qualitative Constraint Calculi
"... Qualitative constraint calculi are representation formalisms that allow for efficient reasoning about spatial and temporal information. Many of the calculi discussed in the field of Qualitative Spatial and Temporal Reasoning can be defined as combinations of other, simpler and more compact formalism ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Qualitative constraint calculi are representation formalisms that allow for efficient reasoning about spatial and temporal information. Many of the calculi discussed in the field of Qualitative Spatial and Temporal Reasoning can be defined as combinations of other, simpler and more compact formalisms. On the other hand, existing calculi can be combined to a new formalism in which one can represent, and reason about, different aspects of a domain at the same time. For example, Gerevini and Renz presented a loose combination of the region connection calculus RCC8 and the point algebra: the resulting formalism integrates topological and qualitative size relations between spatially extended objects. In this paper we compare the approach by Gerevini and Renz to a method that generates a new qualitative calculus by exploiting the semantic interdependencies between the component calculi. We will compare these two methods and analyze some formal relationships between a combined calculus and its components. The paper is completed by an empirical case study in which the reasoning performance of the suggested methods is compared on random test instances. 1
Extending Irregular Cellular Automata with Geometric Proportional Analogies
 Proceedings of the Geographical Information Science Research UK Conference, 11th  13th April 2007, NUI
, 2007
"... We exploit the similarity between irregular Cellular Automata (CA) and Geometric Proportional Analogies (GPA), as both involve manipulations of geometric objects (points, lines and polygons). We describe how each GPA effectively defines a CAlike transition rule and we adapt an algorithm (called Str ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We exploit the similarity between irregular Cellular Automata (CA) and Geometric Proportional Analogies (GPA), as both involve manipulations of geometric objects (points, lines and polygons). We describe how each GPA effectively defines a CAlike transition rule and we adapt an algorithm (called Structure Matching) used for solving