Image Processing: Dealing with Texture

Front Cover
Wiley, Mar 3, 2006 - Technology & Engineering - 634 pages
Self-contained text covering practical image processing methods and theory for image texture analysis.

Techniques for the analysis of texture in digital images are essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences. In biological vision, texture is an important cue allowing humans to discriminate objects. This is because the brain is able to decipher important variations in data at scales smaller than those of the viewed objects. In order to deal with texture in digital data, many techniques have been developed by image processing researchers.

With a wholly practical approach and many worked examples, Image Processing: Dealing with Texture is a comprehensive guide to these techniques, including chapters on mathematical morphology, fractals, Markov random fields, Gabor functions and wavelets. Structured around a series of questions and answers, enabling readers to easily locate information on specific problems, this book also:

  • provides detailed descriptions of methods used to analyse binary as well as grey texture images
  • presents information on two levels: an easy-to-follow narrative explaining the basics, and an advanced, in-depth study of mathematical theorems and concepts
  • looks at ‘good’ and ‘bad’ image processing practice, with wrongly designed algorithms illustrating ‘what not to do’
  • includes an accompanying website, setting out all algorithms discussed within the text.

An ideal self-teaching aid for senior undergraduate and Masters students taking courses in image processing and pattern recognition, this book is also an ideal reference for PhD students, electrical and biomedical engineers, mathematicians, and informatics researchers designing image processing applications.

From inside the book

 

Contents

Binary textures
11
How may the 1D Boolean model be used to describe textures?
45
6 Parameter estimation for the discrete 1D Boolean model
52

37 other sections not shown

Common terms and phrases

algorithm amorous hippopotamus applied autocorrelation function average Boolean model boxes central pixel centre characterise clusters co-occurrence matrices column compute configurations construct convolution filter corresponding created curve Daubechies defined dilation discrete Fourier transform edgels energy eroded erosion estimated flat structuring element fractal dimension frequency band Gabor function Gaussian mask Gaussian window Gibbs distribution given by equation grey value h₂ histogram image of figure imaginary iterations Kaiser window Koch snowflake low pass Markov Markov random field matrix mother wavelet multiply neighbourhood neighbours non-zero number of pixels obtain original image original signal outer product output panel pattern spectrum pixels position power spectrum probability density function produce random field range result of example right-hand side sample scaling sequence shown in figure shows Step subspace Table texture total number U₁ U₂ variable of integration wavelet vectors Wigner distribution zero Σ Σ ΣΣ

References to this book

About the author (2006)

Maria Petrou, University of Surrey, UK
Maria Petrou is Professor of Image Analysis at the University of Surrey. She is the author of Wiley's Image Processing: Fundamentals textbook (1999). She has published over 300 articles on image processing and is Programme Committee Chair for ICPR 2004.

Pedro Garcia-Sevilla, Department of Computer Science, Jaume III University, Castellon, Spain Lectures on the topic of image texture analysis. Gained his PhD under the supervision of Professor Petrou.

Bibliographic information

TitleImage Processing: Dealing with Texture
AuthorsMaria M. P. Petrou, Pedro Garcia Sevilla
Editionillustrated
PublisherWiley, 2006
Original fromthe University of Michigan
DigitizedDec 3, 2007
ISBN0470026286, 9780470026281
Length634 pages
Subjects › 

Technology & Engineering / Electrical
Technology & Engineering / Imaging Systems
  
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