Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44081
Title: Improving a leaves automatic recognition process using PCA
Authors: Solé-Casals, Jordi
Travieso, Carlos M. 
Alonso, Jesús B. 
Ferrer, Miguel A. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Principal Component Analysis Pattern Recognition Leaves Recognition Parameterization Characteristics selection
Issue Date: 2009
Publisher: 1615-3871
Journal: Advances in Soft Computing 
Conference: 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 08) 
Abstract: 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
Source: Advances in Soft Computing[ISSN 1615-3871],v. 49, p. 243-251
Appears in Collections:Actas de congresos
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