@Bibtex-file{Graphics/optica.bib,
  title =        "Bibliography of {"}Integrating Qualitative and
                 Quantitative Shape Recovery{"}",
  author-1 =     "Sven J. Dickinson",
  address-1 =    "Department of Computer Science\\University of
                 Toronto\\Toronto, Ontario, Canada M5S 1A4",
  author-2 =     "Dimitri Metaxas",
  address-2 =    "Department of Computer and Information
                 Science\\University of Pennsylvania\\Philadelphia, PA
                 19104-6389",
  supported =    "no",
  abstract =     "Recent work in qualitative shape recovery and object
                 recognition has focused on solving the ``what is it''
                 problem, while avoiding the ``where is it'' problem. In
                 contrast, typical CAD-based recognition systems have
                 focused on the ``where is it'' problem, while assuming
                 they know what the object is. Although each approach
                 addresses an important aspect of the 3-D object
                 recognition problem, each falls short in addressing the
                 complete problem of recognizing and localizing 3-D
                 objects from a large database. In this paper, we first
                 synthesize a new approach to shape recovery for 3-D
                 object recognition that decouples recognition from
                 localization by combining basic elements from these two
                 approaches. Specifically, we use qualitative shape
                 recovery and recognition techniques to provide strong
                 fitting constraints on physics-based deformable model
                 recovery techniques. Secondly, we extend our previously
                 developed technique of fitting deformable models to
                 occluding image contours to the case of image data
                 captured under general orthographic, perspective, and
                 stereo projections. On one hand, integrating
                 qualitative knowledge of the object being fitted to the
                 data, along with knowledge of occlusion supports a much
                 more robust and accurate quantitative fitting. On the
                 other hand, recovering object pose and quantitative
                 surface shape not only provides a richer description
                 for indexing, but supports interaction with the world
                 when object manipulation is required. This paper
                 presents the approach in detail and applies it to real
                 imagery.",
  comment =      "The \htmladdnormallink{paper}
                 {ftp://relay.cs.toronto.edu:/vis/sven/kluwer/paper4.tex.Z}
                 is also available",
  keywords =     "qualitative and quantitative shape recovery,
                 physics-based modeling, deformable model fitting,
                 object representation, object recognition",
}