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Organizing Large Structural Modelbases
April 1995 (vol. 17 no. 4)
pp. 321-332
ASCII Text
x 
 
Kuntal Sengupta, Kim L. Boyer, "Organizing Large Structural Modelbases," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 4, pp. 321-332, April, 1995.
 
 
BibTex
x
 
@article{ 10.1109/34.385984,
author = {Kuntal Sengupta and Kim L. Boyer},
title = {Organizing Large Structural Modelbases},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {17},
number = {4},
issn = {0162-8828},
year = {1995},
pages = {321-332},
doi = {http://doi.ieeecomputersociety.org/10.1109/34.385984},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Organizing Large Structural Modelbases
IS - 4
SN - 0162-8828
SP321
EP332
EPD - 321-332
A1 - Kuntal Sengupta,
A1 - Kim L. Boyer,
PY - 1995
KW - Object recognition
KW - model based recognition
KW - organizing modelbases
KW - structural descriptions
KW - large modelbases.
VL - 17
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 

Abstract—We present a hierarchically structured approach to organizing large structural modelbases using an information theoretic criterion. Objects (patterns) are modeled in the form of random parametric structural descriptions (RPSDs), an extension of the parametric structural description graph-theoretic formalism [1]. Objects in scenes are modeled as parametric structural descriptions (PSDs). The organization process is driven by pairwise dissimilarity values between RPSDs. We also introduce the node pointer lists, which are computed offline during modelbase organization. During recognition, the only exponential matching process involved is between the scene PSD and the RPSD at the root of the organized tree. Using the organized hierarchy along with the node pointer lists, the remaining work simplifies to a series of inexpensive linear tests at the subsequent levels of the tree search. We develop the theory and present three modelbases: 50 objects built from real image data, 100 CAD models, and 1000 synthetic abstract models.

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Index Terms:
Object recognition, model based recognition, organizing modelbases, structural descriptions, large modelbases.
Citation:
Kuntal Sengupta, Kim L. Boyer, "Organizing Large Structural Modelbases," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 4, pp. 321-332, Apr. 1995, doi:10.1109/34.385984
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