Presenter : YAN-SHOU SIE Authors : Marco Piastra 2013. NN
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Transcript of Presenter : YAN-SHOU SIE Authors : Marco Piastra 2013. NN
Intelligent Database Systems Lab
Presenter : YAN-SHOU SIE
Authors : MARCO PIASTRA
2013. NN
Self-organizing adaptive map: Autonomous learning of curves and surfaces from point samples
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OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Motivation
• In here we want from a point cloud image to reconstruct it original structure, but preliminary version SOAM algorithm is can not effective to produced the expected topology.
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Objectives
• In here we present a improve version SOAM algorithm, its has a much more predictability and includes some new concepts.
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Methodology• Topological and geometrical background Term– homeomorphic – manifold – Voronoi cell– Delaunay triangulation
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– Restricted Delaunay complex :– Homeomorphism and ε –sample– Witness complex
Methodology
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Methodology– Finite sets of witnesses and noise
• Growing self-organizing networks– Positioning the units: ‘gas-like’ dynamics• adaptation strategy of the first kind
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• second kind of strategy
– Competitive Hebbian learning and dynamic units
– Growing networks, insertion threshold
Methodology
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• Self-Organizing Adaptive Map (SOAM)– Stateful units
Methodology
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– Adaptive insertion thresholds
– The SOAM algorithm
Methodology
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Suppose that we have a document with four concepts: ‘Ad,’‘Bert,’ ‘Cees,’ and ‘Dirk.’ If the window size is 2, the following windows are created for this document: {Ad}, {Ad, Bert}, {Bert, Cees},{Cees, Dirk}, and {Dirk}.ex : ‘System’ appears in documents {1,3,6,8} and windows {1,5,10,14,18,20,28}; ‘Process’ appears in documents {1,3,6,12} and windows
{1,5,12,14,18,25,30}.
the similarities are converted to distances:
Methodology-distance measures-document co-occurrence similarity -window-based similarity
window similarity :document similarity :
Avg = 0.15
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Experiments• Experimental setup
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Experiments– Algorithm behavior
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Experiments– Performances
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Experiments– Undersampling and noise: when things go wrong– Boundaries and non-manifold units
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Conclusions• The SOAM algorithm represents an interesting alternative
to deformable models in that it can effectively deal with changes in topology and execution speedup.
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Comments• Advantages
-SOAM can be dynamically self-growth, and the results will be generated close to the result we want, for the field of 3D technology has considerable value..
• Applications- medical imaging , 3D sample, etc.