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Online Handwritten Signature Verification System: using Gaussian Mixture Model and Longest Common Sub-Sequences
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Place, publisher, year, edition, pages
2017.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-15807OAI: oai:DiVA.org:bth-15807DiVA, id: diva2:1177227
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
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Examiners
Available from: 2018-01-29 Created: 2018-01-24 Last updated: 2018-01-29Bibliographically approved

Open Access in DiVA

BTH2018Shashidhar(1037 kB)221 downloads
File information
File name FULLTEXT01.pdfFile size 1037 kBChecksum SHA-512
9ae8db39d19cc440e606696b703d633e9618b4d2fb5b2933a2de2c15c489e668bb59cbff90700c3545b8d24c10375b4d6f15b354419d5ca2b1b75dcd18430c5d
Type fulltextMimetype application/pdf

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Department of Applied Signal Processing
Signal Processing

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CiteExportLink to record
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