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001 978-1-84628-203-4
003 DE-He213
005 20161121230524.0
007 cr nn 008mamaa
008 100301s2005 xxk| s |||| 0|eng d
020 _a9781846282034
_9978-1-84628-203-4
024 7 _a10.1007/1-84628-203-9
_2doi
050 4 _aQC350-467
050 4 _aTA1501-1820
050 4 _aQC392-449.5
050 4 _aTA1750-1750.22
072 7 _aTTB
_2bicssc
072 7 _aPHJ
_2bicssc
072 7 _aTEC030000
_2bisacsh
082 0 4 _a621.36
_223
100 1 _aMiddleton, Lee.
_eauthor.
245 1 0 _aHexagonal Image Processing
_h[electronic resource] :
_bA Practical Approach /
_cby Lee Middleton, Jayanthi Sivaswamy.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _aXIII, 254 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Pattern Recognition
505 0 _aCurrent 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.
520 _aHexagonal 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.
650 0 _aPhysics.
650 0 _aComputer graphics.
650 0 _aImage processing.
650 0 _aMultimedia systems.
650 0 _aOptics.
650 0 _aOptoelectronics.
650 0 _aPlasmons (Physics).
650 1 4 _aPhysics.
650 2 4 _aOptics, Optoelectronics, Plasmonics and Optical Devices.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aMedia Design.
700 1 _aSivaswamy, Jayanthi.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852339142
830 0 _aAdvances in Pattern Recognition
856 4 0 _uhttp://dx.doi.org/10.1007/1-84628-203-9
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c500032
_d500032