Analysis and Characterization of Embedded Vision Systems for Taxonomy Formulation
2013 (English)In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Nasser Kehtarnavaz, Matthias F. Carlsohn,, USA: SPIE - International Society for Optical Engineering, 2013, Art. no. 86560J- p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
USA: SPIE - International Society for Optical Engineering, 2013. Art. no. 86560J- p.
, Proceedings of SPIE, ISSN 0277-786X ; 8656
System taxonomy, Smart cameras, Embedded vision systems, Wireless vision sensor networks
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:miun:diva-16035DOI: 10.1117/12.2000584ISI: 000333051900018ScopusID: 2-s2.0-84875855354Local ID: STCISBN: 978-0-8194-9429-0OAI: oai:DiVA.org:miun-16035DiVA: diva2:513132
Real-Time Image and Video Processing 2013; Burlingame, CA; United States; 6 February 2013 through 7 February 2013; Code 96385