Novel "Squiral" (Square Spiral) Architecture for Fast Image Processing

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Abstract

Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as "squiral") architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as "Squiral" Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed "squiral" architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the e�ciency of the proposed SIP framework compared with standard convolution.
LanguageEnglish
Pages371-381
JournalJournal of Visual Communication and Image Representation
Volume49
Early online date28 Sep 2017
DOIs
Publication statusE-pub ahead of print - 28 Sep 2017

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Image processing
Convolution
Pixels
Processing

Keywords

  • square spiral ("squiral") image processing (SIP)
  • spiral addressing scheme
  • eye tremor
  • non-overlapping convolution
  • fast image processing

Cite this

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title = "Novel {"}Squiral{"} (Square Spiral) Architecture for Fast Image Processing",
abstract = "Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as {"}squiral{"}) architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as {"}Squiral{"} Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed {"}squiral{"} architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the e�ciency of the proposed SIP framework compared with standard convolution.",
keywords = "square spiral ({"}squiral{"}) image processing (SIP), spiral addressing scheme, eye tremor, non-overlapping convolution, fast image processing",
author = "Min Jing and Scotney Bryan and SA Coleman and T.Martin McGinnity",
year = "2017",
month = "9",
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doi = "10.1016/j.jvcir.2017.09.014",
language = "English",
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pages = "371--381",
journal = "Journal of Visual Communication and Image Representation",
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T1 - Novel "Squiral" (Square Spiral) Architecture for Fast Image Processing

AU - Jing, Min

AU - Bryan, Scotney

AU - Coleman, SA

AU - McGinnity, T.Martin

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N2 - Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as "squiral") architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as "Squiral" Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed "squiral" architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the e�ciency of the proposed SIP framework compared with standard convolution.

AB - Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as "squiral") architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as "Squiral" Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed "squiral" architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the e�ciency of the proposed SIP framework compared with standard convolution.

KW - square spiral ("squiral") image processing (SIP)

KW - spiral addressing scheme

KW - eye tremor

KW - non-overlapping convolution

KW - fast image processing

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