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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From the reviews:
"Middleton and Sivaswamy have attempted to provide a comprehensive survey of the existing literature and a sound theoretical basis for using hexagonal sampling in image processing. ... By covering all of the areas of image processing that a basic image processing text would cover, the authors have succeeded in providing a good reference and a framework for students interested in processing images using hexagonal sampling. The authors provide several examples of advantages of processing using the hexagonal sampling grid over a square sampling grid." (Gopinath Kuduvalli, Computing Reviews, December, 2006)
"Middleton and Sivaswamy have attempted to provide a comprehensive survey of the existing literature and a sound theoretical basis for using hexagonal sampling in image processing. ... By covering all of the areas of image processing that a basic image processing text would cover, the authors have succeeded in providing a good reference and a framework for students interested in processing images using hexagonal sampling. The authors provide several examples of advantages of processing using the hexagonal sampling grid over a square sampling grid." (Gopinath Kuduvalli, Computing Reviews, December, 2006)