Scale Sensitivity of Textural Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00471593" target="_blank" >RIV/67985556:_____/17:00471593 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-52277-7_11" target="_blank" >http://dx.doi.org/10.1007/978-3-319-52277-7_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-52277-7_11" target="_blank" >10.1007/978-3-319-52277-7_11</a>
Alternative languages
Result language
angličtina
Original language name
Scale Sensitivity of Textural Features
Original language description
Prevailing surface material recognition methods are based on textural features but most of these features are very sensitive to scale variations and the recognition accuracy significantly declines with scale incompatibility between visual material measurements used for learning and unknown materials to be recognized. This effect of mutual incompatibility between training and testing visual material measurements scale on the recognition accuracy is investigated for leading textural features and verified on a wood database, which contains veneers from sixty-six varied European and exotic wood species. The results show that the presented textural features, which are illumination invariants extracted from a generative multispectral Markovian texture representation, outperform the most common alternatives, such as Local Binary Patterns, Gabor features, or histogram-based approaches.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA14-10911S" target="_blank" >GA14-10911S: Mathematical modeling of surface material appearance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016
ISBN
978-3-319-52276-0
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
84-92
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Lima
Event date
Nov 8, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000418399200011