1996
Jakobovits, Rex; Lewis, Lara; Ahrens, James; Shapiro, Linda; Tanimoto, Steven; Brinkley, James
A visual database environment for scientific research Journal Article
In: Journal of Visual Languages & Computing, vol. 7, no. 4, pp. 361–375, 1996, (LA-UR-pending).
Abstract | Links | BibTeX | Tags: visual database
@article{jakobovits1996visual,
title = {A visual database environment for scientific research},
author = {Rex Jakobovits and Lara Lewis and James Ahrens and Linda Shapiro and Steven Tanimoto and James Brinkley},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AVisualDatabaseEnvironmentForScientificResearch.pdf},
year = {1996},
date = {1996-01-01},
journal = {Journal of Visual Languages & Computing},
volume = {7},
number = {4},
pages = {361--375},
publisher = {Elsevier},
abstract = {This paper describes a visual database environment designed to be used for scientific research in the imaging sciences. It provides hierarchical relational structures that allow the user to model data as entities possessing properties, parts and relationships, and it supports multi-level queries on these structures. A schema constructor interface allows users to define for each structure, not only its components, but also its visualization, which is built from its components using graphical primitives. Finally, an experiment management subsystem allows users to construct and run computa- tional experiments that apply imaging operators to data from the database. The experiment management system keeps track of the experimental procedures developed by the user and the results generated by executing these procedures.},
note = {LA-UR-pending},
keywords = {visual database},
pubstate = {published},
tppubtype = {article}
}
This paper describes a visual database environment designed to be used for scientific research in the imaging sciences. It provides hierarchical relational structures that allow the user to model data as entities possessing properties, parts and relationships, and it supports multi-level queries on these structures. A schema constructor interface allows users to define for each structure, not only its components, but also its visualization, which is built from its components using graphical primitives. Finally, an experiment management subsystem allows users to construct and run computa- tional experiments that apply imaging operators to data from the database. The experiment management system keeps track of the experimental procedures developed by the user and the results generated by executing these procedures.
1994
Shapiro, Linda; Tanimoto, Steven; Brinkley, James; Ahrens, James; Jakobovits, Rex; Lewis, Lara
A visual database system for data and experiment management in model-based computer vision Proceedings Article
In: CAD-Based Vision Workshop, 1994., Proceedings of the 1994 Second, pp. 64–72, IEEE 1994, (LA-UR-pending).
Abstract | Links | BibTeX | Tags: model-based computer vision, visual database
@inproceedings{shapiro1994visual,
title = {A visual database system for data and experiment management in model-based computer vision},
author = {Linda Shapiro and Steven Tanimoto and James Brinkley and James Ahrens and Rex Jakobovits and Lara Lewis},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AVisualDatabaseSystemForDataAndExperimentManagementInModel-BasedComputerVision.pdf},
year = {1994},
date = {1994-01-01},
booktitle = {CAD-Based Vision Workshop, 1994., Proceedings of the 1994 Second},
pages = {64--72},
organization = {IEEE},
abstract = {Computer vision researchers work with many different forms of data. Model-based vision systems work with geometric models of 3D objects, intensity or range images, and many different kinds of features that are extracted from these images. The recognition/pose estimation process involves a number of different steps and different operations all of which take in and generate various forms of data. Figure 1 illustrates the operations and data types required for a sample recognition process (Shapiro, Neal, and Ponder; 1992). The process starts with a gray-scale image and produces an edge image, a line segment structure, and a triple chain structure (described in Section 2). Each object in the model database is represented by a set of its major views, and each major view is represented by a triple chain structure. The triple chain structure that was extracted from the image and the set of triple chain structures representing the major views (view classes) are input to the matching algorithm which tries to identify the view class or classes that most closely match the view in the image. This process illustrates the kind of experiments that modelare simpler than the one shown, and some are much more complex.},
note = {LA-UR-pending},
keywords = {model-based computer vision, visual database},
pubstate = {published},
tppubtype = {inproceedings}
}
Computer vision researchers work with many different forms of data. Model-based vision systems work with geometric models of 3D objects, intensity or range images, and many different kinds of features that are extracted from these images. The recognition/pose estimation process involves a number of different steps and different operations all of which take in and generate various forms of data. Figure 1 illustrates the operations and data types required for a sample recognition process (Shapiro, Neal, and Ponder; 1992). The process starts with a gray-scale image and produces an edge image, a line segment structure, and a triple chain structure (described in Section 2). Each object in the model database is represented by a set of its major views, and each major view is represented by a triple chain structure. The triple chain structure that was extracted from the image and the set of triple chain structures representing the major views (view classes) are input to the matching algorithm which tries to identify the view class or classes that most closely match the view in the image. This process illustrates the kind of experiments that modelare simpler than the one shown, and some are much more complex.
: . .
1.
Jakobovits, Rex; Lewis, Lara; Ahrens, James; Shapiro, Linda; Tanimoto, Steven; Brinkley, James
A visual database environment for scientific research Journal Article
In: Journal of Visual Languages & Computing, vol. 7, no. 4, pp. 361–375, 1996, (LA-UR-pending).
@article{jakobovits1996visual,
title = {A visual database environment for scientific research},
author = {Rex Jakobovits and Lara Lewis and James Ahrens and Linda Shapiro and Steven Tanimoto and James Brinkley},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AVisualDatabaseEnvironmentForScientificResearch.pdf},
year = {1996},
date = {1996-01-01},
journal = {Journal of Visual Languages & Computing},
volume = {7},
number = {4},
pages = {361--375},
publisher = {Elsevier},
abstract = {This paper describes a visual database environment designed to be used for scientific research in the imaging sciences. It provides hierarchical relational structures that allow the user to model data as entities possessing properties, parts and relationships, and it supports multi-level queries on these structures. A schema constructor interface allows users to define for each structure, not only its components, but also its visualization, which is built from its components using graphical primitives. Finally, an experiment management subsystem allows users to construct and run computa- tional experiments that apply imaging operators to data from the database. The experiment management system keeps track of the experimental procedures developed by the user and the results generated by executing these procedures.},
note = {LA-UR-pending},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper describes a visual database environment designed to be used for scientific research in the imaging sciences. It provides hierarchical relational structures that allow the user to model data as entities possessing properties, parts and relationships, and it supports multi-level queries on these structures. A schema constructor interface allows users to define for each structure, not only its components, but also its visualization, which is built from its components using graphical primitives. Finally, an experiment management subsystem allows users to construct and run computa- tional experiments that apply imaging operators to data from the database. The experiment management system keeps track of the experimental procedures developed by the user and the results generated by executing these procedures.
2.
Shapiro, Linda; Tanimoto, Steven; Brinkley, James; Ahrens, James; Jakobovits, Rex; Lewis, Lara
A visual database system for data and experiment management in model-based computer vision Proceedings Article
In: CAD-Based Vision Workshop, 1994., Proceedings of the 1994 Second, pp. 64–72, IEEE 1994, (LA-UR-pending).
@inproceedings{shapiro1994visual,
title = {A visual database system for data and experiment management in model-based computer vision},
author = {Linda Shapiro and Steven Tanimoto and James Brinkley and James Ahrens and Rex Jakobovits and Lara Lewis},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AVisualDatabaseSystemForDataAndExperimentManagementInModel-BasedComputerVision.pdf},
year = {1994},
date = {1994-01-01},
booktitle = {CAD-Based Vision Workshop, 1994., Proceedings of the 1994 Second},
pages = {64--72},
organization = {IEEE},
abstract = {Computer vision researchers work with many different forms of data. Model-based vision systems work with geometric models of 3D objects, intensity or range images, and many different kinds of features that are extracted from these images. The recognition/pose estimation process involves a number of different steps and different operations all of which take in and generate various forms of data. Figure 1 illustrates the operations and data types required for a sample recognition process (Shapiro, Neal, and Ponder; 1992). The process starts with a gray-scale image and produces an edge image, a line segment structure, and a triple chain structure (described in Section 2). Each object in the model database is represented by a set of its major views, and each major view is represented by a triple chain structure. The triple chain structure that was extracted from the image and the set of triple chain structures representing the major views (view classes) are input to the matching algorithm which tries to identify the view class or classes that most closely match the view in the image. This process illustrates the kind of experiments that modelare simpler than the one shown, and some are much more complex.},
note = {LA-UR-pending},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Computer vision researchers work with many different forms of data. Model-based vision systems work with geometric models of 3D objects, intensity or range images, and many different kinds of features that are extracted from these images. The recognition/pose estimation process involves a number of different steps and different operations all of which take in and generate various forms of data. Figure 1 illustrates the operations and data types required for a sample recognition process (Shapiro, Neal, and Ponder; 1992). The process starts with a gray-scale image and produces an edge image, a line segment structure, and a triple chain structure (described in Section 2). Each object in the model database is represented by a set of its major views, and each major view is represented by a triple chain structure. The triple chain structure that was extracted from the image and the set of triple chain structures representing the major views (view classes) are input to the matching algorithm which tries to identify the view class or classes that most closely match the view in the image. This process illustrates the kind of experiments that modelare simpler than the one shown, and some are much more complex.