
Proceedings Paper
Analysis and characterization of embedded vision systems for taxonomy formulationFormat | Member Price | Non-Member Price |
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Paper Abstract
The current trend in embedded vision systems is to propose bespoke solutions for specific problems as each application
has different requirement and constraints. There is no widely used model or benchmark which aims to facilitate generic
solutions in embedded vision systems. Providing such model is a challenging task due to the wide number of use cases,
environmental factors, and available technologies. However, common characteristics can be identified to propose an
abstract model. Indeed, the majority of vision applications focus on the detection, analysis and recognition of objects.
These tasks can be reduced to vision functions which can be used to characterize the vision systems. In this paper, we
present the results of a thorough analysis of a large number of different types of vision systems. This analysis led us to
the development of a system’s taxonomy, in which a number of vision functions as well as their combination
characterize embedded vision systems. To illustrate the use of this taxonomy, we have tested it against a real vision
system that detects magnetic particles in a flowing liquid to predict and avoid critical machinery failure. The proposed
taxonomy is evaluated by using a quantitative parameter which shows that it covers 95 percent of the investigated vision
systems and its flow is ordered for 60 percent systems. This taxonomy will serve as a tool for classification and
comparison of systems and will enable the researchers to propose generic and efficient solutions for same class of
systems.
Paper Details
Date Published: 19 February 2013
PDF: 11 pages
Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 86560J (19 February 2013); doi: 10.1117/12.2000584
Published in SPIE Proceedings Vol. 8656:
Real-Time Image and Video Processing 2013
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)
PDF: 11 pages
Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 86560J (19 February 2013); doi: 10.1117/12.2000584
Show Author Affiliations
Muhammad Imran, Mid Sweden Univ. (Sweden)
Khaled Benkrid, The Univ. of Edinburgh (United Kingdom)
Khursheed Khursheed, Mid Sweden Univ. (Sweden)
Khaled Benkrid, The Univ. of Edinburgh (United Kingdom)
Khursheed Khursheed, Mid Sweden Univ. (Sweden)
Naeem Ahmad, Mid Sweden Univ. (Sweden)
Mattias O’Nils, Mid Sweden Univ. (Sweden)
Najeem Lawal, Mid Sweden Univ. (Sweden)
Mattias O’Nils, Mid Sweden Univ. (Sweden)
Najeem Lawal, Mid Sweden Univ. (Sweden)
Published in SPIE Proceedings Vol. 8656:
Real-Time Image and Video Processing 2013
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)
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