The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Patrick Doherty - One of the best experts on this subject based on the ideXlab platform.
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Stream-Based Hierarchical Anchoring
KI - Künstliche Intelligenz, 2013Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring . In this paper we present a stream-based hierarchical anchoring framework. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.
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A stream-based hierarchical anchoring framework
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols labeling physical objects and the sensor data being collected about them, a process called anchoring. In this paper we present a stream-based hierarchical anchoring framework extending the DyKnow knowledge processing middleware. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach has been applied to a traffic monitoring application where an unmanned aerial vehicle collects information about a small urban area in order to detect traffic violations.
Fredrik Heintz - One of the best experts on this subject based on the ideXlab platform.
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Stream-Based Hierarchical Anchoring
KI - Künstliche Intelligenz, 2013Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring . In this paper we present a stream-based hierarchical anchoring framework. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.
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A stream-based hierarchical anchoring framework
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols labeling physical objects and the sensor data being collected about them, a process called anchoring. In this paper we present a stream-based hierarchical anchoring framework extending the DyKnow knowledge processing middleware. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach has been applied to a traffic monitoring application where an unmanned aerial vehicle collects information about a small urban area in order to detect traffic violations.
Paklok Poon - One of the best experts on this subject based on the ideXlab platform.
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On the effectiveness of Classification trees for test case construction
Information & Software Technology, 1998Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:Abstract The notion of the Classification-Hierarchy table and the Classification-tree construction algorithm provide a systematic approach to the construction of Classification trees from given sets of Classifications and their associated classes. Using Classification trees, the set of all possible test cases can be constructed from functional specifications. This paper extends their study by introducing a metric to measure the effectiveness of a Classification tree with respect to the construction of test cases, and providing ways to improve this effectiveness.
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construction of Classification trees via the Classification Hierarchy table
Information & Software Technology, 1997Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:Abstract The Classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of Classification trees. This paper supplements their studies by proposing a methodology to construct Classification trees systematically from given sets of Classifications and their associated classes, via the notion of the Classification-Hierarchy table. The intuition of the Classification-Hierarchy table is to capture the hierarchical relationship for every pair of distinct Classifications from which Classification trees can be constructed.
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APSEC - Improving the quality of Classification trees via restructuring
Proceedings 1996 Asia-Pacific Software Engineering Conference, 1996Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:The Classification-Hierarchy table developed by Chen and Poon (1996) provides a systematic approach to construct Classification trees from given sets of Classifications and their associated classes. The paper enhances their study by defining a metric to measure the "quality" of a Classification tree, and providing an algorithm to improve this quality.
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Australian Software Engineering Conference - Classification-Hierarchy Table: a methodology for constructing the Classification tree
Proceedings of 1996 Australian Software Engineering Conference, 1996Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:The Classification-Tree Method developed by Grochtmann and Grimm (1993) provides a systematic approach to produce test cases based on the functional specification. This paper supplements the Classification-Tree Method by proposing a methodology to construct a Classification-tree from a given set of Classifications and classes based on the notion of Classification-Hierarchy Table. The Classification-Hierarchy Table is used to capture the Hierarchy of a Classification tree. From this table, useful information such as the relative level of the Classifications in the tree, their parent Classifications and parent classes could be obtained to guide the construction of the Classification tree.
Jonas Kvarnström - One of the best experts on this subject based on the ideXlab platform.
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Stream-Based Hierarchical Anchoring
KI - Künstliche Intelligenz, 2013Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring . In this paper we present a stream-based hierarchical anchoring framework. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.
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A stream-based hierarchical anchoring framework
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Fredrik Heintz, Jonas Kvarnström, Patrick DohertyAbstract:Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols labeling physical objects and the sensor data being collected about them, a process called anchoring. In this paper we present a stream-based hierarchical anchoring framework extending the DyKnow knowledge processing middleware. A Classification Hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of Classification, permitting symbolic reasoning on different levels of abstraction. The approach has been applied to a traffic monitoring application where an unmanned aerial vehicle collects information about a small urban area in order to detect traffic violations.
Tsong Yueh Chen - One of the best experts on this subject based on the ideXlab platform.
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On the effectiveness of Classification trees for test case construction
Information & Software Technology, 1998Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:Abstract The notion of the Classification-Hierarchy table and the Classification-tree construction algorithm provide a systematic approach to the construction of Classification trees from given sets of Classifications and their associated classes. Using Classification trees, the set of all possible test cases can be constructed from functional specifications. This paper extends their study by introducing a metric to measure the effectiveness of a Classification tree with respect to the construction of test cases, and providing ways to improve this effectiveness.
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construction of Classification trees via the Classification Hierarchy table
Information & Software Technology, 1997Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:Abstract The Classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of Classification trees. This paper supplements their studies by proposing a methodology to construct Classification trees systematically from given sets of Classifications and their associated classes, via the notion of the Classification-Hierarchy table. The intuition of the Classification-Hierarchy table is to capture the hierarchical relationship for every pair of distinct Classifications from which Classification trees can be constructed.
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APSEC - Improving the quality of Classification trees via restructuring
Proceedings 1996 Asia-Pacific Software Engineering Conference, 1996Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:The Classification-Hierarchy table developed by Chen and Poon (1996) provides a systematic approach to construct Classification trees from given sets of Classifications and their associated classes. The paper enhances their study by defining a metric to measure the "quality" of a Classification tree, and providing an algorithm to improve this quality.
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Classification-Hierarchy Table: a methodology for constructing the Classification tree
Proceedings of 1996 Australian Software Engineering Conference, 1996Co-Authors: Tsong Yueh Chen, P.l. PoonAbstract:The Classification-Tree Method developed by Grochtmann and Grimm (1993) provides a systematic approach to produce test cases based on the functional specification. This paper supplements the Classification-Tree Method by proposing a methodology to construct a Classification-tree from a given set of Classifications and classes based on the notion of Classification-Hierarchy Table. The Classification-Hierarchy Table is used to capture the Hierarchy of a Classification tree. From this table, useful information such as the relative level of the Classifications in the tree, their parent Classifications and parent classes could be obtained to guide the construction of the Classification tree.
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Australian Software Engineering Conference - Classification-Hierarchy Table: a methodology for constructing the Classification tree
Proceedings of 1996 Australian Software Engineering Conference, 1996Co-Authors: Tsong Yueh Chen, Paklok PoonAbstract:The Classification-Tree Method developed by Grochtmann and Grimm (1993) provides a systematic approach to produce test cases based on the functional specification. This paper supplements the Classification-Tree Method by proposing a methodology to construct a Classification-tree from a given set of Classifications and classes based on the notion of Classification-Hierarchy Table. The Classification-Hierarchy Table is used to capture the Hierarchy of a Classification tree. From this table, useful information such as the relative level of the Classifications in the tree, their parent Classifications and parent classes could be obtained to guide the construction of the Classification tree.