Welcome to the InfoSci Platform
Reference Hub1
Implementation of Biologically Inspired Components in Embedded Vision Systems

Implementation of Biologically Inspired Components in Embedded Vision Systems

Christopher Wing Hong Ngau, Li-Minn Ang, Kah Phooi Seng
Copyright: © 2013 |Pages: 39
ISBN13: 9781466625396|ISBN10: 1466625392|EISBN13: 9781466625402
DOI: 10.4018/978-1-4666-2539-6.ch013
Cite Chapter Cite Chapter

MLA

Ngau, Christopher Wing Hong, et al. "Implementation of Biologically Inspired Components in Embedded Vision Systems." Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts, edited by Marc Pomplun and Junichi Suzuki, IGI Global, 2013, pp. 307-345. https://doi.org/10.4018/978-1-4666-2539-6.ch013

APA

Ngau, C. W., Ang, L., & Seng, K. P. (2013). Implementation of Biologically Inspired Components in Embedded Vision Systems. In M. Pomplun & J. Suzuki (Eds.), Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts (pp. 307-345). IGI Global. https://doi.org/10.4018/978-1-4666-2539-6.ch013

Chicago

Ngau, Christopher Wing Hong, Li-Minn Ang, and Kah Phooi Seng. "Implementation of Biologically Inspired Components in Embedded Vision Systems." In Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts, edited by Marc Pomplun and Junichi Suzuki, 307-345. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2539-6.ch013

Export Reference

Mendeley
Favorite

Abstract

Studies in the area of computational vision have shown the capability of visual attention (VA) processing in aiding various visual tasks by providing a means for simplifying complex data handling and supporting action decisions using readily available low-level features. Due to the inclusion of computational biological vision components to mimic the mechanism of the human visual system, VA processing is computationally complex with heavy memory requirements and is often found implemented in workstations with unapplied resource constraints. In embedded systems, the computational capacity and memory resources are of a primary concern. To allow VA processing in such systems, the chapter presents a low complexity, low memory VA model based on an established mainstream VA model that addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA processing in an environment with limited resources. Lastly, a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented low complexity, low memory VA model.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.