Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44081
Título: Improving a leaves automatic recognition process using PCA
Autores/as: Solé-Casals, Jordi
Travieso, Carlos M. 
Alonso, Jesús B. 
Ferrer, Miguel A. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Principal Component Analysis Pattern Recognition Leaves Recognition Parameterization Characteristics selection
Fecha de publicación: 2009
Editor/a: 1615-3871
Publicación seriada: Advances in Soft Computing 
Conferencia: 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 08) 
Resumen: In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.
URI: http://hdl.handle.net/10553/44081
ISBN: 9783540858607
ISSN: 1615-3871
DOI: 10.1007/978-3-540-85861-4_29
Fuente: Advances in Soft Computing[ISSN 1615-3871],v. 49, p. 243-251
Colección:Actas de congresos
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