000			03103nam a22003257a 4500
001			20136365
003			SA-PMU
005			20260217101931.0
008			260217b ||||| |||| 00| 0 eng d
010			_a 2017049755
020			_a9780128149768 _qh/c
020			_z9780128149768
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aTA1634 .N59 2020
082	0	0	_a006.3/7 _223
245	0	0	_aFeature extraction and image processing for computer vision / _cMark S. Nixon, Electronics and Computer Science, University of Southampton ; Alberto S. Aguado, Foundry, London.
260			_bAcademic Press
264		1	_bEngineering Science Reference, _c2020
300			_a632 pages ;
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aFeature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation.
504			_aIncludes index.
520			_aFeature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. - The only text to concentrate on feature extraction with working implementation and worked through mathematical derivations and algorithmic methods - A thorough overview of available feature extraction methods including essential background theory, shape methods, texture and deep learning - Up to date coverage of interest point detection, feature extraction and description and image representation (including frequency domain and colour) - Good balance between providing a mathematical background and practical implementation - Detailed and explanatory of algorithms in MATLAB and Python
650		0	_aComputer vision.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBOOK
999			_c12880 _d12880