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A Coarse-to-Fine Strategy for Multiclass Shape Detection
December 2004 (vol. 26 no. 12)
pp. 1606-1621
ASCII Text
x 
 
Yali Amit, Donald Geman, Xiaodong Fan, "A Coarse-to-Fine Strategy for Multiclass Shape Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1606-1621, December, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2004.111,
author = {Yali Amit and Donald Geman and Xiaodong Fan},
title = {A Coarse-to-Fine Strategy for Multiclass Shape Detection},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {12},
issn = {0162-8828},
year = {2004},
pages = {1606-1621},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.111},
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 - A Coarse-to-Fine Strategy for Multiclass Shape Detection
IS - 12
SN - 0162-8828
SP1606
EP1621
EPD - 1606-1621
A1 - Yali Amit,
A1 - Donald Geman,
A1 - Xiaodong Fan,
PY - 2004
KW - Shape detection
KW - multiple classes
KW - statistical model
KW - spread edges
KW - coarse-to-fine search
KW - online competition.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Multiclass shape detection, in the sense of recognizing and localizing instances from multiple shape classes, is formulated as a two-step process in which local indexing primes global interpretation. During indexing a list of instantiations (shape identities and poses) is compiled, constrained only by no missed detections at the expense of false positives. Global information, such as expected relationships among poses, is incorporated afterward to remove ambiguities. This division is motivated by computational efficiency. In addition, indexing itself is organized as a coarse-to-fine search simultaneously in class and pose. This search can be interpreted as successive approximations to likelihood ratio tests arising from a simple ("naive Bayes”) statistical model for the edge maps extracted from the original images. The key to constructing efficient "hypothesis tests” for multiple classes and poses is local ORing; in particular, spread edges provide imprecise but common and locally invariant features. Natural tradeoffs then emerge between discrimination and the pattern of spreading. These are analyzed mathematically within the model-based framework and the whole procedure is illustrated by experiments in reading license plates.

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Index Terms:
Shape detection, multiple classes, statistical model, spread edges, coarse-to-fine search, online competition.
Citation:
Yali Amit, Donald Geman, Xiaodong Fan, "A Coarse-to-Fine Strategy for Multiclass Shape Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1606-1621, Dec. 2004, doi:10.1109/TPAMI.2004.111
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