|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  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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