Ebook: Hexagonal Image Processing: A Practical Approach

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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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.

---

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.

Content:
Front Matter....Pages I-XIII
Introduction....Pages 1-4
Current approaches to vision....Pages 5-25
The Proposed HIP Framework....Pages 27-69
Image processing within the HIP framework....Pages 71-103
Applications of the HIP framework....Pages 105-126
Practical aspects of hexagonal image processing....Pages 127-149
Processing images on square and hexagonal grids - a comparison....Pages 151-196
Conclusion....Pages 197-199
Back Matter....Pages 201-254

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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.

Content:
Front Matter....Pages I-XIII
Introduction....Pages 1-4
Current approaches to vision....Pages 5-25
The Proposed HIP Framework....Pages 27-69
Image processing within the HIP framework....Pages 71-103
Applications of the HIP framework....Pages 105-126
Practical aspects of hexagonal image processing....Pages 127-149
Processing images on square and hexagonal grids - a comparison....Pages 151-196
Conclusion....Pages 197-199
Back Matter....Pages 201-254
....