02978nam a22002897a 450000100090000000300070000900500170001600800410003301000170007402000230009102000180011404000280013204200080016005000210016808200160018924501810020526000190038626400410040530000160044633600260046233700280048833800270051650007540054350400200129752013500131765000210266720136365SA-PMU20260217101931.0260217b           ||||| |||| 00| 0 eng d  a  2017049755  a9780128149768qh/c  z9780128149768  aDLCbengcDLCerdadDLC  apcc00aTA1634 .N59 202000a006.3/722300aFeature extraction and image processing for computer vision / cMark S. Nixon, Electronics and Computer Science, University of Southampton ; Alberto S. Aguado, Foundry, London.  bAcademic Press 1bEngineering Science Reference,c2020  a632 pages ;  atextbtxt2rdacontent  aunmediatedbn2rdamedia  avolumebnc2rdacarrier  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.
  aIncludes index.  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 0aComputer vision.