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Hexagonal Image Processing [electronic resource] :A Practical Approach / by Lee Middleton, Jayanthi Sivaswamy.

by Middleton, Lee [author.]; Sivaswamy, Jayanthi [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advances in Pattern Recognition: Publisher: London : Springer London, 2005.Description: XIV, 254p. 116 illus. online resource.ISBN: 9781846282034.Subject(s): Computer science | Computer vision | Computer Science | Image Processing and Computer Vision | Computer Imaging, Vision, Pattern Recognition and Graphics | Media DesignDDC classification: 006.6 | 006.37 Online resources: Click here to access online
Contents:
Current approaches to vision -- The Proposed HIP Framework -- Image processing within the HIP framework -- Applications of the HIP framework -- Practical aspects of hexagonal image processing -- Processing images on square and hexagonal grids - a comparison -- Conclusion.
In: Springer eBooksSummary: Hexagonal Image Processing provides an introduction to the processing of hexagonally sampled images, includes a survey of the work done in the field, and presents a novel framework for hexagonal image processing (HIP) based on hierarchical aggregates. Digital image processing is currently dominated by the use of square sampling lattices, however, hexagonal sampling lattices can also be used to define digital images. The strengths offered by hexagonal lattices over square lattices are considerable: • higher packing density, • uniform connectivity of points (pixels) in the lattice, • better angular resolution by virtue of having more nearest neighbours, and • superlative representation of curves. The utility of the HIP framework is demonstrated by implementing several basic image processing techniques (for the spatial and frequency domain) and some applications. The HIP framework serves as a tool for comparing processing of images defined on a square vs hexagonal grid, to determine their relative merits and demerits. The theory and algorithms covered are supplemented by attention to practical details such as accommodating hardware that support only images sampled on a square lattice. Including a Foreword written by Professor Narendra Ahuja, an eminent researcher in the field of Image Processing and Computer Vision, the book’s fresh approach to the subject offers insight and workable know-how to both researchers and postgraduates.
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Current approaches to vision -- The Proposed HIP Framework -- Image processing within the HIP framework -- Applications of the HIP framework -- Practical aspects of hexagonal image processing -- Processing images on square and hexagonal grids - a comparison -- Conclusion.

Hexagonal Image Processing provides an introduction to the processing of hexagonally sampled images, includes a survey of the work done in the field, and presents a novel framework for hexagonal image processing (HIP) based on hierarchical aggregates. Digital image processing is currently dominated by the use of square sampling lattices, however, hexagonal sampling lattices can also be used to define digital images. The strengths offered by hexagonal lattices over square lattices are considerable: • higher packing density, • uniform connectivity of points (pixels) in the lattice, • better angular resolution by virtue of having more nearest neighbours, and • superlative representation of curves. The utility of the HIP framework is demonstrated by implementing several basic image processing techniques (for the spatial and frequency domain) and some applications. The HIP framework serves as a tool for comparing processing of images defined on a square vs hexagonal grid, to determine their relative merits and demerits. The theory and algorithms covered are supplemented by attention to practical details such as accommodating hardware that support only images sampled on a square lattice. Including a Foreword written by Professor Narendra Ahuja, an eminent researcher in the field of Image Processing and Computer Vision, the book’s fresh approach to the subject offers insight and workable know-how to both researchers and postgraduates.

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