Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43962
Title: Parameterization methodology for 2D shape classification by hidden Markov models
Authors: Ferrer, Miguel A. 
Alonso, Jesus B. 
David, Sebastien
Travieso, Carlos M. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hidden Markov models , Synchronization , Abstracts , Pattern recognition , Shape , Tutorials , NIST
Issue Date: 2015
Publisher: 2219-5491
Journal: European Signal Processing Conference
Conference: 12th European Signal Processing Conference, EUSIPCO 2004 
Abstract: In computer vision, two-dimensional shape classification is a complex and well known topic, often basic for three-dimensional object recognition. Among different classification methods, this paper is focus on those that describe the 2D shape by means of a sequence of d-dimensional vectors which feeds a left to right hidden Markov model (HMM) recogniser. We propose a methodology for featuring the 2D shape with a sequence of vectors that take advantage of the HMM ability to spot the times when the infrequent vectors of the input sequence of vectors occur. This propierty is deduced by the repetition of the same HMM state during the moments in which the infrequent vectors is repeated. These HMM states are called by us synchronism states. The synchronization between the HMM and the input sequence of vectors can be improved thanks to adding an index component to the vectors. We show the recognition rate improvement of our proposal on selected applications
URI: http://hdl.handle.net/10553/43962
ISBN: 9783200001657
ISSN: 2219-5491
Source: European Signal Processing Conference[ISSN 2219-5491],v. 06-10-September-2004 (7079990), p. 761-764
Appears in Collections:Actas de congresos
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