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


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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.


Objectives

• In here we present a improve version SOAM algorithm, its has a much more predictability and includes some new concepts.


Methodology• Topological and geometrical background Term– homeomorphic – manifold – Voronoi cell– Delaunay triangulation


– Restricted Delaunay complex ：– Homeomorphism and ε –sample– Witness complex

Methodology


Methodology– Finite sets of witnesses and noise

• Growing self-organizing networks– Positioning the units: ‘gas-like’ dynamics• adaptation strategy of the first kind


• second kind of strategy

– Competitive Hebbian learning and dynamic units

– Growing networks, insertion threshold

Methodology


• Self-Organizing Adaptive Map (SOAM)– Stateful units

Methodology


– Adaptive insertion thresholds

– The SOAM algorithm

Methodology


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


Experiments• Experimental setup


Experiments– Algorithm behavior


Experiments– Performances


Experiments– Undersampling and noise: when things go wrong– Boundaries and non-manifold units


Conclusions• The SOAM algorithm represents an interesting alternative

to deformable models in that it can effectively deal with changes in topology and execution speedup.


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.

Documents

Presenter : YAN-SHOU SIE Authors : Marco Piastra 2013. NN