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Biologically Inspired Components in Embedded Vision Systems

Biologically Inspired Components in Embedded Vision Systems

Li-Minn Ang, Kah Phooi Seng, Christopher Wing Hong Ngau
Copyright: © 2018 |Pages: 36
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch018
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MLA

Ang, Li-Minn, et al. "Biologically Inspired Components in Embedded Vision Systems." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 458-493. https://doi.org/10.4018/978-1-5225-5204-8.ch018

APA

Ang, L., Seng, K. P., & Ngau, C. W. (2018). Biologically Inspired Components in Embedded Vision Systems. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 458-493). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch018

Chicago

Ang, Li-Minn, Kah Phooi Seng, and Christopher Wing Hong Ngau. "Biologically Inspired Components in Embedded Vision Systems." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 458-493. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch018

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Abstract

Biological vision components like visual attention (VA) algorithms aim to mimic the mechanism of the human vision system. Often VA algorithms are complex and require high computational and memory requirements to be realized. In biologically-inspired vision and embedded systems, the computational capacity and memory resources are of a primary concern. This paper presents a discussion for implementing VA algorithms in embedded vision systems in a resource constrained environment. The authors survey various types of VA algorithms and identify potential techniques which can be implemented in embedded vision systems. Then, they propose a low complexity and low memory VA model based on a well-established mainstream VA model. The proposed model addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA in a resource constrained environment. Finally a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented model.

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