TY - BOOK ID - 208016 TI - Hexagonal image processing : a practical approach AU - Middleton, Lee AU - Sivaswamy, Jayanthi. PY - 2005 SN - 1280412461 9786610412464 1846282039 1852339144 1447158849 PB - London : Springer, DB - UniCat KW - Image processing KW - Hexagons. KW - Digital techniques. KW - Six-sided polygon KW - Polygons KW - Digital image processing KW - Digital electronics KW - Computer vision. KW - Computer science. KW - Optics, Lasers, Photonics, Optical Devices. KW - Image Processing and Computer Vision. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Media Design. KW - Informatics KW - Science KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Pattern recognition systems KW - Lasers. KW - Photonics. KW - Optical data processing. KW - Multimedia systems . KW - Computer-based multimedia information systems KW - Multimedia computing KW - Multimedia information systems KW - Multimedia knowledge systems KW - Information storage and retrieval systems KW - Optical computing KW - Visual data processing KW - Bionics KW - Electronic data processing KW - Integrated optics KW - Photonics KW - Computers KW - New optics KW - Optics KW - Light amplification by stimulated emission of radiation KW - Masers, Optical KW - Optical masers KW - Light amplifiers KW - Light sources KW - Optoelectronic devices KW - Nonlinear optics KW - Optical parametric oscillators KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:208016 AB - 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. ER -