Authors:
Andrzej Florek
and
Tomasz Piascik
Affiliation:
Institute of Control and Information Engineering, Poznan University of Technology, Poland
Keyword(s):
Shape description, signature, object recognition, classification.
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 paper, a simple and efficient approach to classify planar shapes is proposed. This approach is based
on comparison of areas of dynamicly sampled classic signatures. Presented approach is dedicated to the recognition of convex and concave planar shapes, containing openings in the area enclosed by boundary.
A way to calculate the discrete representation of classic distance-versus-angle signatures, a reduction of memory requirements and a number of calculations are presented. Analysis carried out from classification experiments applied to images of real objects (car-engine collector seals) indicates good properties of dissimilarity coefficients, based on modified signature, taken as an object descriptor.