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Seeing the Future with Imaging Science Interdisciplinary Research Team Summaries (2011) / Chapter Skim
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IDR Team Summary 8: Develop image-specialized database tools for data stewardship and system design in large-scale applications.
Pages 101-106

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 101...
... imagers has made the acquisition of visible band images nearly free; still and video images of the natural environment and social groups are being acquired at an unprecedented rate. These are being used for mobile visual search applications, in which users acquire cell phone images to navigate their local environment.
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From page 102...
... • Are there common architectural and computational needs across multiple types of imaging modalities for storing, validating quality, and analyzing image databases? Are there general ontologies for imaging data that might be derived from the images themselves, rather than by labels added by the users in the metadata?
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From page 103...
... Of course, the Harvard Computers weren't quite like the ones we have today -- they were, in fact, a group of women, hired by the astronomer Edward Charles Pickering to process astronomical data. Just as today's computers analyze images and extract meaningful information, Pickering's team went through one glass-plate photograph at a time identifying, measuring, and recording what they saw in the stars.
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From page 104...
... One recent example is the Sloan Digital Sky Survey, in which a dedicated telescope photographed over a quarter of the night sky and catalogued more than 350 million celestial objects. The resulting dataset has yielded some profound discoveries, including the universe's most distant quasars and large populations of sub-stellar objects.
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From page 105...
... One of these is Digital Imaging and Communications in Medicine, or DICOM, a standard developed in the 1980s to standardize file formats and metadata. DICOM allows medical images acquired at different places to be transferred and pooled in collective databases.
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From page 106...
... c. Agile exploratory tools that incorporate image analysis and ma chine learning must be imagined and implemented for imaging databases.
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Key Terms

  • imaging science
  • imaging data
  • digital imaging
  • feature extraction
  • meaningful information

  • data sharing
  • imaging technologies
  • image analysis
  • modest amount
  • database creation

  • relational databases
  • file formats
  • online june
  • exploratory tools
  • evolving scientific

  • accessed online
  • medical images
  • celestial objects
  • widely accessible
  • standardize file

  • vendors accommodate
  • thereby enabling
  • acquire cell
  • software database
  • images themselves

  • browse images
  • astronomers organize
  • cell phone
  • exabyte scale
  • mobile visual


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