Shape Based Machine Vision (bibtex)
by Robert Sablatnig
Abstract:
The study of visual object recognition is often motivated by the problem of recognizing 3-d objects given that we receive 2-d patterns of light on our retinae. Recent findings from human psychophysics, neurophysiology and computational vision provide converging evi-dence for a view-based recognition framework in which objects and scenes are represented as collections of viewpoint-specific local features rather than 2-d templates or 3-d mod-els. Hence the recent decade saw a gradual shift away from the 3-d object reconstruction approach pioneered by Marr toward view-based approaches. This report summarizes our contributions to this problem where we focus on the shape as recognition feature and apply these findings in the area of Machine Vision. The first part presents an overview of the framework, motivates the view-based recognition strategy, and introduces the hierachical matching concept. Next, a short summary of a collection of six representative publications of our work carried out in this field, and a discussion of how this fits into the framework is given. The second part consists of the six papers themselves, where we start with a paper on the general framework which is followed by three different applications of the framework in Visual Inspection, Archaeology and Art History. The remaining two papers describe re-cent work performed in 3-d vision as part of the object-based recognition concept. The first paper is on the registration of range data, in which we propose a novel technique for range image registration. The collection ends with a work on combining different 3-d acquisition techniques within the hierarchical framework.
Reference:
Shape Based Machine Vision (Robert Sablatnig), Technical report, PRIP, TU Wien, 2003.
Bibtex Entry:
@TechReport{TR080,
  author =	 "Robert Sablatnig",
  title =	 "Shape {B}ased {M}achine {V}ision",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-080",
  year =	 "2003",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr80.pdf",
  abstract =	 "The study of visual object recognition is often
                  motivated by the problem of recognizing 3-d objects
                  given that we receive 2-d patterns of light on our
                  retinae. Recent findings from human psychophysics,
                  neurophysiology and computational vision provide
                  converging evi-dence for a view-based recognition
                  framework in which objects and scenes are
                  represented as collections of viewpoint-specific
                  local features rather than 2-d templates or 3-d
                  mod-els. Hence the recent decade saw a gradual shift
                  away from the 3-d object reconstruction approach
                  pioneered by Marr toward view-based approaches. This
                  report summarizes our contributions to this problem
                  where we focus on the shape as recognition feature
                  and apply these findings in the area of Machine
                  Vision. The first part presents an overview of the
                  framework, motivates the view-based recognition
                  strategy, and introduces the hierachical matching
                  concept. Next, a short summary of a collection of
                  six representative publications of our work carried
                  out in this field, and a discussion of how this fits
                  into the framework is given. The second part
                  consists of the six papers themselves, where we
                  start with a paper on the general framework which is
                  followed by three different applications of the
                  framework in Visual Inspection, Archaeology and Art
                  History. The remaining two papers describe re-cent
                  work performed in 3-d vision as part of the
                  object-based recognition concept. The first paper is
                  on the registration of range data, in which we
                  propose a novel technique for range image
                  registration. The collection ends with a work on
                  combining different 3-d acquisition techniques
                  within the hierarchical framework. ",
}
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