Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/46034
Título: A bark recognition algorithm for plant classification using a least square support vector machine
Autores/as: Blaanco, Luis J.
Travieso, Carlos M. 
Quinteiro, Jose M. 
Hernandez, Pablo V. 
Dutta, Malay Kishore
Singh, Anushikha
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Classification algorithm
Support vector machines
Discrete wavelet transforms
Biology computing
Pattern classification
Fecha de publicación: 2017
Publicación seriada: 2016 Ninth International Conference On Contemporary Computing (Ic3)
Conferencia: 9th International Conference on Contemporary Computing, IC3 2016 
Resumen: In this paper, a bark recognition algorithm for plant classification is presented. A Least-Square Support Vector Machine (LSSVM) with image and data processing techniques is used to implement a general purpose automated classifier. Using a data base of 40 sections of photographs taken of each of the 23 species, we applied an algorithm to homogenize the illumination of the images. After applying it, we obtained a 256-elements array from the Local Binary Pattern (LBP) histogram. Each element of the array was introduced in the LSSVM for classification. The success rate of the resultant recognizer is 82.38%.
URI: http://hdl.handle.net/10553/46034
ISBN: 9781509032518
ISSN: 2572-6110
DOI: 10.1109/IC3.2016.7880233
Fuente: 2016 9th International Conference on Contemporary Computing, IC3 2016 (7880233)
Colección:Actas de congresos
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