Implementation of biological inspired components in embedded vision systems

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

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationDeveloping and applying biologically-inspired vision systems
Subtitle of host publication Interdisciplinary concepts
EditorsMarc Pomplun, Junichi Suzuki
Place of PublicationUnited States
PublisherIGI Global
Number of pages39
ISBN (Electronic)9781466625402
ISBN (Print)9781466625396
Publication statusPublished - 2013

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