Prof. Allen Klinger Profile

Prof. Allen Klinger

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Proceedings Article | 7 April 1995 Paper

Data structures for gigabyte systems

Allen Klinger

Proceedings Volume 2410, (1995) https://doi.org/10.1117/12.205984

KEYWORDS: Visualization, Data communications, Communication engineering, Image enhancement, Image transmission, Image compression, Solids, Computer graphics, Photography, Shape analysis

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This paper involves data structures in planning to combine engineering research areas considered as communication modes: image, outline-sketches, and speech. Images are enhanced compressed and transmitted, but in graphics solid display is central, while in speech recognition/identification dominate. Outside computing, graphics uses sketch, outline-drawing, or schematic summaries of other data (photographic images). Practical image-processing involves comparisons, features/edges, shape, and segmentation, using both transforms and other global analyses. Most speech work involves domain restriction. This limit can be deleted by focussing on data structures: they can link word and picture domains, and allow for captioning, for indexing/highlighting-domains to users. This shows data structures enable implementing useful functions, support information-handling with synergistic benefits: the paper's theme. Data structuring is also the theme of recent research literature on alternate means for visual presentation of multiple-measure numerical data. This paper briefly surveys these materials. We show how research from the data structure field enables new methods for addressing visualization issues, improves large-record data-handling, and aids greater use of visual and numerical records. (This expands on a talk presented 8 July 1994 at Argonne National Laboratory.)

Proceedings Article | 1 October 1990 Paper

Preference voting for sensor fusion

Gregory Baraghimian, Allen Klinger

Proceedings Volume 1306, (1990) https://doi.org/10.1117/12.21628

KEYWORDS: Sensors, Image fusion, Image sensors, Sensor fusion, Information fusion, Sensor performance, Binary data, Information visualization, Visualization, Signal processing

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This paper compares preference voting techniques for multi-sensor decision support. The domain we are concerned with is enabling integration of visual information from diverse kinds of sensors. We define conditions imposed by: a) multi-source information fusion tasks, and b) models of multi-source decision-making processes. The results are directed toward two key problems: a) facilitating group decisions via computer support, and b) selection of related items from an image data base. We describe three methods for combining multiple assessments of image content from different sources. Two of these make use of diversity of knowledge possessed by contributers to the group decision. We assume a heterogeneous voter pool (individuals or programs), and also different credibilities for their inputs to the group decision. We present simulation results for the three decision-making methods.

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