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Image processing and analysis : a primer / Georgy Gimelʹfarb, Patrice Delmas, The University of Auckland, New Zealand.

By: Contributor(s): Series: Primers in electronics and computer science ; volume 3Publisher: London ; Hackensack, NJ : World Scientific Publishing Europe Ltd., ©2019Description: xvi, 229 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781786345813
Subject(s): DDC classification:
  • 621.367 G42 2019 23
LOC classification:
  • TA1630 .G555 2019
Contents:
Note continued: 2.4.Questions and exercises -- 3.Filtering to Denoise or Enhance -- 3.1.Modelling image noise -- 3.2.Moving window transform (MWT) -- 3.3.Linear filtering -- 3.3.1.Mean filter -- 3.3.2.Fast box filtering -- 3.3.3.Gaussian filter -- 3.4.Nonlinear filtering -- 3.4.1.Median filter -- 3.4.2.Rank filtering -- 3.5.Questions and exercises -- 4.Filtering to Segment -- 4.1.Measuring segmentation accuracy -- 4.2.Modelling image regions -- 4.3.Histogram thresholding -- 4.3.1.Adaptive object-background thresholding -- 4.3.2.Gaussian mixture model -- 4.3.3.Unimodal thresholding -- 4.4.Colour segmentation -- 4.4.1.RGB-HSI colour representations -- 4.4.2.Colour thresholding -- 4.4.3.Multi-region segmentation -- 4.5.Labelling connected regions -- 4.6.Questions and exercises -- 5.Morphological Filtering -- 5.1.Binary morphology -- 5.1.1.Structuring element -- 5.1.2.Erosion -- 5.1.3.Dilation -- 5.1.4.Opening -- 5.1.5.Closing -- 5.1.6.Hit-and-miss transform Note continued: 5.2.Greyscale morphology -- 5.2.1.Erosion -- 5.2.2.Dilation -- 5.2.3.Opening and closing -- 5.2.4.Top-hat and dual top-hat transforms -- 5.3.Questions and exercises -- 6.Deforming Boundaries to Segment -- 6.1.Evolving an AC -- 6.1.1.Parametric ACs -- 6.1.2.Geometric (level-set) ACs -- 6.2.Active shape models -- 6.2.1.Derivation of the PDM -- 6.2.2.Registration of training images -- 6.2.3.Using PCA to determine a PDM -- 6.2.4.Matching an ASM to an unseen image -- 6.3.Questions and exercises -- 7.Filtering to Find Points-of-Interest -- 7.1.Differential image properties: Gradients and Hessians -- 7.1.1.Gradients and Hessians in a digital image -- 7.1.2.DoG, DoOG, and LoG -- 7.1.3.Inverse USAN areas -- 7.2.Edge detection -- 7.2.1.Thresholding gradients -- 7.2.2.Canny edge detector -- 7.3.Detecting POIs -- 7.3.1.Rotation-invariant POIs -- 7.3.2.Scale-rotation invariance: SIFT and SURF -- 7.4.Questions and exercises -- 8.Transforming Image Plane Note continued: 8.1.Signal interpolation for restoring continuous images -- 8.2.Basic geometric transformations -- 8.2.1.Forward and backward coordinate mapping -- 8.2.2.Affine transformations -- 8.2.3.Polynomial warping -- 8.3.Questions and exercises -- 9.Spectra and Spectral Filtering -- 9.1.ID complex exponentials -- 9.2.2D/3D exponentials -- 9.3.Discrete Fourier transform (DFT) -- 9.3.1.Amplitude, phase, and power spectra -- 9.3.2.Some properties of the DFT -- 9.3.3.Discrete cosine transform (DCT) -- 9.3.4.Fast Fourier transform (FFT) -- 9.4.Wavelets and wavelet decompositions -- 9.4.1.Multi-resolution analysis (MRA) -- 9.4.2.Haar wavelets -- 9.4.3.Smooth wavelets -- 9.5.Filtering in the spectral domain -- 9.5.1.Convolution in the spectral domain -- 9.5.2.Low-, high-, and band-pass spectral filtering -- 9.5.3.Inverse and Wiener filters -- 9.5.4.Homomorphic and cepstral filters -- 9.6.Questions and exercises -- Appendix A Further Reading -- A.1.More diverse sources Note continued: A.2.Bibliographical and historical notes -- Appendix B Symbols and Math Notation -- B.1.Latin symbols -- B.2.Greek symbols -- B.3.Math notation -- Appendix C Abbreviations.
Summary: "This textbook guides readers through their first steps into the challenging world of mimicking human vision with computational tools and techniques pertaining to the field of image processing and analysis. While today's theoretical and applied processing and analysis of images meet with challenging and complex problems, this primer is confined to a much simpler, albeit critical, collection of image-to-image transformations, including image normalisation, enhancement, and filtering. It serves as an introduction to beginners, a refresher for undergraduate and graduate students, as well as engineers and computer scientists confronted with a problem to solve in computer vision. The book covers basic image processing/computer vision pipeline techniques, which are widely used in today's computer vision, computer graphics, and image processing, giving the readers enough knowledge to successfully tackle a wide range of applied problems."--
Holdings
Item type Current library Collection Call number Status Barcode
Books Books College Library General Circulation Section GC GC 621.367 G42 2019 (Browse shelf(Opens below)) Available HNU002843

Includes bibliographical references (pages 223-226) and index.

Note continued: 2.4.Questions and exercises --
3.Filtering to Denoise or Enhance --
3.1.Modelling image noise --
3.2.Moving window transform (MWT) --
3.3.Linear filtering --
3.3.1.Mean filter --
3.3.2.Fast box filtering --
3.3.3.Gaussian filter --
3.4.Nonlinear filtering --
3.4.1.Median filter --
3.4.2.Rank filtering --
3.5.Questions and exercises --
4.Filtering to Segment --
4.1.Measuring segmentation accuracy --
4.2.Modelling image regions --
4.3.Histogram thresholding --
4.3.1.Adaptive object-background thresholding --
4.3.2.Gaussian mixture model --
4.3.3.Unimodal thresholding --
4.4.Colour segmentation --
4.4.1.RGB-HSI colour representations --
4.4.2.Colour thresholding --
4.4.3.Multi-region segmentation --
4.5.Labelling connected regions --
4.6.Questions and exercises --
5.Morphological Filtering --
5.1.Binary morphology --
5.1.1.Structuring element --
5.1.2.Erosion --
5.1.3.Dilation --
5.1.4.Opening --
5.1.5.Closing --
5.1.6.Hit-and-miss transform Note continued: 5.2.Greyscale morphology --
5.2.1.Erosion --
5.2.2.Dilation --
5.2.3.Opening and closing --
5.2.4.Top-hat and dual top-hat transforms --
5.3.Questions and exercises --
6.Deforming Boundaries to Segment --
6.1.Evolving an AC --
6.1.1.Parametric ACs --
6.1.2.Geometric (level-set) ACs --
6.2.Active shape models --
6.2.1.Derivation of the PDM --
6.2.2.Registration of training images --
6.2.3.Using PCA to determine a PDM --
6.2.4.Matching an ASM to an unseen image --
6.3.Questions and exercises --
7.Filtering to Find Points-of-Interest --
7.1.Differential image properties: Gradients and Hessians --
7.1.1.Gradients and Hessians in a digital image --
7.1.2.DoG, DoOG, and LoG --
7.1.3.Inverse USAN areas --
7.2.Edge detection --
7.2.1.Thresholding gradients --
7.2.2.Canny edge detector --
7.3.Detecting POIs --
7.3.1.Rotation-invariant POIs --
7.3.2.Scale-rotation invariance: SIFT and SURF --
7.4.Questions and exercises --
8.Transforming Image Plane Note continued: 8.1.Signal interpolation for restoring continuous images --
8.2.Basic geometric transformations --
8.2.1.Forward and backward coordinate mapping --
8.2.2.Affine transformations --
8.2.3.Polynomial warping --
8.3.Questions and exercises --
9.Spectra and Spectral Filtering --
9.1.ID complex exponentials --
9.2.2D/3D exponentials --
9.3.Discrete Fourier transform (DFT) --
9.3.1.Amplitude, phase, and power spectra --
9.3.2.Some properties of the DFT --
9.3.3.Discrete cosine transform (DCT) --
9.3.4.Fast Fourier transform (FFT) --
9.4.Wavelets and wavelet decompositions --
9.4.1.Multi-resolution analysis (MRA) --
9.4.2.Haar wavelets --
9.4.3.Smooth wavelets --
9.5.Filtering in the spectral domain --
9.5.1.Convolution in the spectral domain --
9.5.2.Low-, high-, and band-pass spectral filtering --
9.5.3.Inverse and Wiener filters --
9.5.4.Homomorphic and cepstral filters --
9.6.Questions and exercises --
Appendix A Further Reading --
A.1.More diverse sources Note continued: A.2.Bibliographical and historical notes --
Appendix B Symbols and Math Notation --
B.1.Latin symbols --
B.2.Greek symbols --
B.3.Math notation --
Appendix C Abbreviations.

"This textbook guides readers through their first steps into the challenging world of mimicking human vision with computational tools and techniques pertaining to the field of image processing and analysis. While today's theoretical and applied processing and analysis of images meet with challenging and complex problems, this primer is confined to a much simpler, albeit critical, collection of image-to-image transformations, including image normalisation, enhancement, and filtering. It serves as an introduction to beginners, a refresher for undergraduate and graduate students, as well as engineers and computer scientists confronted with a problem to solve in computer vision. The book covers basic image processing/computer vision pipeline techniques, which are widely used in today's computer vision, computer graphics, and image processing, giving the readers enough knowledge to successfully tackle a wide range of applied problems."--

College of Engineering and Computer Studies Bachelor of Science in Computer Science

Text in English

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