Improving a Leaves Automatic Recognition Process Using PCA
Improving a Leaves Automatic Recognition Process Using PCA
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.
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Percepció de les formes, 3307 Tecnología electrónica, Principal Component Analysis Pattern Recognition Leaves Recognition Parameterization Characteristics selection
Percepció de les formes, 3307 Tecnología electrónica, Principal Component Analysis Pattern Recognition Leaves Recognition Parameterization Characteristics selection
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