Spherical Self-Organizing Maps - Sangole, Archana
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The thesis documents various implementations of the spherical self-organizing feature map spanning from range imagery to multi-dimensional satellite imagery. Other implementations include archaeological data and face modeling. The techniques are adapted versions of the fundamental learning strategy of the self-organizing map, introduced by Dr. Teuvo Kohonen, and implemented within a spherical topology. Continuity in learning is imposed by the predefined spherical lattice which also transforms the data into a 3D tessellated form. Since the tessellated forms originate from a sphere they simplify…mehr

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Produktbeschreibung
The thesis documents various implementations of the
spherical self-organizing feature map spanning from
range imagery to multi-dimensional satellite imagery.
Other implementations include archaeological data
and face modeling. The techniques are adapted
versions of the fundamental learning strategy of the
self-organizing map, introduced by Dr. Teuvo Kohonen,
and implemented within a spherical topology.
Continuity in learning is imposed by the predefined
spherical lattice which also transforms the data into
a 3D tessellated form. Since the tessellated forms
originate from a sphere they simplify the computation
of transformation parameters, object re-orientation
etc. commonly used in three-dimensional graphics.
Autorenporträt
Dr. Sangole is a Researcher in Mechanical Engineering at Ecole
Polytechnique and the Marie-Enfant Rehabilitation Center of
CHU-Sainte-Justine, Montreal, QC. Her research is in
musculoskeletal biomechanics. She is a manufacturing engineer
with previous research experience in deformable modeling, data
visualization and Computer-Aided-Design exchange.