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Pankaj K. Agarwal
LSRC D214A
Phone: (919) 660-6540 |
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| The course is intended to provide
a systematic introduction to the modeling and algorithmic
techniques behind the geometric and statistical analysis
of 3D shapes that arise in many applications including
molecular biology, computer graphics, and computer aided
design. The primary emphasis will be on recent algorithms
developed for representing, analyzing, comparing, classifying,
and indexing 3D shapes. The topics covered will include: |
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| I. |
Shape representation:
basic representation methods, shape simplification, hierarchical
methods, deformable shapes. |
| II. |
Shape descriptors:
histograms, harmonic maps, distance distribution, medial axis,
topology based methods. |
| III. |
Statistical shape analysis:
Shape space, coordinate systems, procrustes distances and their
generalizations, deformations. |
| IV. |
Shape matching and registration:
Combinatorial methods, geometric hashing, ICP and its variants,
graph matching, entropy based methods. |
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Shape classification and
clustering: Geometric clustering, graph based methods,
spectral methods, decision trees, support vector machines. |
| VI. |
Shape indexing:
Indexing multidimensional data, proximity search, search engines.
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| The course is open to graduate and undergraduate
students from all fields. |
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Assignments: 20%
weight
Two assignments will be given during the semester, which each student
has to complete individually.
Research Project:
40% weight
The intention is to produce a work of publishable or near-publishable
quality. It will consist of two parts:
(i) proposal and survey
(ii) research work (a final paper and presentation)
Lectures: 40%
weight
Each student will be asked to give one or two lectures, which will
require reading papers on a given topic and presenting the technqiues
and results. |
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