Author:
Toon Goedemé
Affiliation:
De Nayer Technical University, Embedded System Design (EmSD); Katholieke Universiteit Leuven, VISICS, ESAT/PSI, Belgium
Keyword(s):
Waste sorting, local image features, SURF, SIFT, visual words.
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, we present a method for fast and robust object recognition, especially developed for implementation on an embedded platform. As an example, the method is applied to the automatic sorting of consumer waste. Out of a stream of different thrown-away food packages, specific items — in this case beverage cartons — can be visually recognised and sorted out. To facilitate and optimise the implementation of this algorithm on an embedded platform containing parallel hardware, we developed a voting scheme for constellations of visual words, i.e. clustered local features (SURF in this case). On top of easy implementation and robust and fast performance, even with large databases, an extra advantage is that this method can handle multiple identical visual features in one model.