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Cited 31 time in webofscience Cited 41 time in scopus
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Shape Matching Using Multiscale Integral Invariants

Authors
Hong, Byung-WooSoatto, Stefano
Issue Date
Jan-2015
Publisher
IEEE COMPUTER SOC
Keywords
Shape matching; shape descriptor; integral invariant; scale invariant; Wasserstein distance
Citation
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.37, no.1, pp 151 - 160
Pages
10
Journal Title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume
37
Number
1
Start Page
151
End Page
160
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/10009
DOI
10.1109/TPAMI.2014.2342215
ISSN
0162-8828
1939-3539
Abstract
We present a shape descriptor based on integral kernels. Shape is represented in an implicit form and it is characterized by a series of isotropic kernels that provide desirable invariance properties. The shape features are characterized at multiple scales which form a signature that is a compact description of shape over a range of scales. The shape signature is designed to be invariant with respect to group transformations which include translation, rotation, scaling, and reflection. In addition, the integral kernels that characterize local shape geometry enable the shape signature to be robust with respect to undesirable perturbations while retaining discriminative power. Use of our shape signature is demonstrated for shape matching based on a number of synthetic and real examples.
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소프트웨어대학 (AI학과)
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