Biologically inspired components in embedded vision systems

Li Minn Ang, Kah Phooi Seng, Christopher Wing Hong Ngau

    Research output: Book chapter/Published conference paperChapter in textbook/reference bookpeer-review


    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.
    Original languageEnglish
    Title of host publicationComputer Vision
    Subtitle of host publicationConcepts, methodologies, tools, and applications
    EditorsInformation Resources Managment Association
    Place of PublicationUnited States
    PublisherIGI Global
    Number of pages36
    ISBN (Electronic)9781522552055
    ISBN (Print)9781522552048
    Publication statusPublished - 01 Jan 2018


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