Authors:
Jordi Solé-Casals
1
;
Carlos M. Travieso
2
;
Miguel A. Ferrer
2
;
Jesús B. Alonso
3
and
Juan Carlos Briceño
4
Affiliations:
1
University of Vic, Spain
;
2
University of Las Palmas de Gran Canaria, Spain
;
3
, Spain
;
4
Computer Science Department, University of Costa Rica, Costa Rica
Keyword(s):
Independent Component Analysis, Pattern Recognition, Leaves Recognition, Parameterization, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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 to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.