Hexagonal Image Processing - Info and Reading Options
A Practical Approach (Advances in Pattern Recognition)
By Lee Middleton and Jayanthi Sivaswamy
"Hexagonal Image Processing" is published by Springer in July 13, 2005, it has 254 pages and the language of the book is English.
“Hexagonal Image Processing” Metadata:
- Title: Hexagonal Image Processing
- Authors: Lee MiddletonJayanthi Sivaswamy
- Language: English
- Number of Pages: 254
- Publisher: Springer
- Publish Date: July 13, 2005
“Hexagonal Image Processing” Subjects and Themes:
- Subjects: ➤ Digital techniques - Image processing - Hexagons - Computer science - Computer vision - Image Processing and Computer Vision - Computer Imaging, Vision, Pattern Recognition and Graphics - Media Design
Edition Specifications:
- Format: Hardcover
- Weight: 1.2 pounds
- Dimensions: 9.3 x 6.3 x 0.7 inches
Edition Identifiers:
- The Open Library ID: OL8974477M - OL16944384W
- Online Computer Library Center (OCLC) ID: 57484393
- Library of Congress Control Number (LCCN): 2005923261
- ISBN-13: 9781852339142
- ISBN-10: 1852339144
- All ISBNs: 1852339144 - 9781852339142
AI-generated Review of “Hexagonal Image Processing”:
Snippets and Summary:
The perceptual mechanisms used by different biological organisms to negotiate the visual world are fascinatingly diverse.
"Hexagonal Image Processing" Description:
The Open Library:
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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