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The neighborhood viewer: a paradigm for exploring image databases
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Source Conference on Human Factors in Computing Systems archive
CHI '97 extended abstracts on Human factors in computing systems: looking to the future table of contents
Atlanta, Georgia
SESSION: Late-breaking/short talks table of contents
Pages: 299 - 300  
Year of Publication: 1997
ISBN:0-89791-926-2
Authors
John V. Carlis  University of Minnesota, Minneapolis, MN
Alex Safonov  University of Minnesota, Minneapolis, MN
Douglas Perrin  University of Minnesota, Minneapolis, MN
Joseph A. Konstan  University of Minnesota, Minneapolis, MN
Sponsor
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 10,   Citation Count: 1
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ABSTRACT

The Brain Neighborhood Viewer is a tool developed to help neuroscientists explore massive databases of brain images. The viewer implements an interface paradigm based on stacks of 2D images that are "yoked together" to provide a common coordinate system. When a user navigates in an image stack, all yoked stacks are updated to display the same location, which we call a brain neighborhood. Experience with the neighborhood suggests that this interface is useful for neuroscience research.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Alex Safonov, Douglas Perrin, John Carlis, Joseph Konstan, John Riedl and Robert Elde, Lessons from the Neighborhood Viewer: Building Innovative Collaborative Applications in Tcl and Tk, Proceedings of the USENIX Tcl/Tk Workshop 96

CITED BY
Andreas Becks , Christian Seeling, SWAPit: a multiple views paradigm for exploring associations of texts and structured data, Proceedings of the working conference on Advanced visual interfaces, May 25-28, 2004, Gallipoli, Italy

INDEX TERMS

Keywords:
brain neighborhood viewer, browsing, image databases, multi-resolution images, scientific visualization

Collaborative Colleagues:
John V. Carlis: colleagues
Alex Safonov: colleagues
Douglas Perrin: colleagues
Joseph A. Konstan: colleagues

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