Authors:
Rodrigo D. C. da Silva
;
George A. P. Thé
and
Fátima N. S. de Medeiros
Affiliation:
Federal University of Ceara, Brazil
Keyword(s):
Independent Component Analysis, Invariant Rotation, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Industrial Automation and Robotics
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
Independent component analysis (ICA) is a recent technique used in signal processing for feature
description in classification systems, as well as in signal separation, with applications ranging from
computer vision to economics. In this paper we propose a preprocessing step in order to make ICA
algorithm efficient for rotation invariant feature description of images. Tests were carried out on five
datasets and the extracted descriptors were used as inputs to the k-nearest neighbor (k-NN) classifier.
Results showed an increasing trend on the recognition rate, which approached 100%. Additionally, when
low-resolution images acquired from an industrial time-of-flight sensor are used, the recognition rate
increased up to 93.33%.