3D Shape Analysis for Quantification, Classification, and Retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Indriyati Atmosukarto",
  title =        "{3D} Shape Analysis for Quantification,
                 Classification, and Retrieval",
  school =       "Computer Science and Engineering, University of
  year =         "2010",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf",
  size =         "139 pages",
  abstract =     "Three-dimensional objects are now commonly used in a
                 large number of applications including games,
                 mechanical engineering, archaeology, culture, and even
                 medicine. As a result, researchers have started to
                 investigate the use of 3D shape descriptors that aim to
                 encapsulate the important shape properties of the 3D
                 objects. This thesis presents new 3D shape
                 representation methodologies for quantification,
                 classification and retrieval tasks that are flexible
                 enough to be used in general applications, yet detailed
                 enough to be useful in medical craniofacial
                 dysmorphology studies. The methodologies begin by
                 computing low-level features at each point of the 3D
                 mesh and aggregating the features into histograms over
                 mesh neighbourhoods. Two different methodologies are
                 defined. The first methodology begins by learning the
                 characteristics of salient point histograms for each
                 particular application, and represents the points in a
                 2D spatial map based on longitude-latitude
                 transformation. The second methodology represents the
                 3D objects by using the global 2D histogram of the
                 azimuth-elevation angles of the surface normals of the
                 points on the 3D objects.

                 Four datasets, two craniofacial datasets and two
                 general 3D object datasets, were obtained to develop
                 and test the different shape analysis methods developed
                 in this thesis. Each dataset has different shape
                 characteristics that help explore the different
                 properties of the methodologies. Experimental results
                 on classifying the craniofacial datasets show that our
                 methodologies achieve higher classification accuracy
                 than medical experts and existing state-of-the-art 3D
                 descriptors. Retrieval and classification results using
                 the general 3D objects show that our methodologies are
                 comparable to existing view-based and feature-based
                 descriptors and outperform these descriptors in some
                 cases. Our methodology can also be used to speed up the
                 most powerful general 3D object descriptor to date.",
  notes =        "GPLAB, Matlab",

Genetic Programming entries for Indriyati Atmosukarto