Following the success of the first edition, this thoroughly updated second edition of
Image Processing: The Fundamentals will ensure that it remains the ideal text for anyone seeking an introduction to the essential concepts of image processing. New material includes image processing and colour, sine and cosine transforms, Independent Component Analysis (ICA), phase congruency and the monogenic signal and several other new topics. These updates are combined with coverage of classic topics in image processing, such as orthogonal transforms and image enhancement, making this a truly comprehensive text on the subject.
Presents material at two levels of difficulty: the main text addresses the fundamental concepts and presents a broad view of image processing, whilst more advanced material is interleaved in boxes throughout the text, providing further reference for those who wish to examine each technique in depth.Contains a large number of fully worked out examples.Focuses on an understanding of how image processing methods work in practice.Illustrates complex algorithms on a step-by-step basis, and lists not only the good practices but also identifies the pitfalls in each case.Uses a clear question and answer structure.Includes a CD containing the MATLAB (R) code of the various examples and algorithms presented in the book. There is also an accompanying website with slides available for download for instructors as a teaching resource.
Image Processing: The Fundamentals, Second Edition is an ideal teaching resource for both undergraduate and postgraduate students. It will also be of value to researchers of various disciplines from medicine to mathematics with a professional interest in image processing
Preface. 1 Introduction. 2 Image Transformations. 2.1 Singular value decomposition. 2.2 Haar, Walsh and Hadamard transforms. 2.3 Discrete Fourier transform. 2.4 The even symmetric discrete cosine transform (EDCT). 2.5 The odd symmetric discrete cosine transform (ODCT). 2.6 The even antisymmetric discrete sine transform (EDST). 2.7 The odd antisymmetric discrete sine transform (ODST). 3 Statistical Description of Images. 3.1 Random fields. 3.2 Karhunen-Loeve transform. 3.3 Independent component analysis. 4 Image Enhancement. 4.1 Elements of linear filter theory. 4.2 Reducing high frequency noise. 4.3 Reducing low frequency interference. 4.4 Histogram manipulation. 4.5 Generic deblurring algorithms. 5 Image Restoration. 5.1 Homogeneous linear image restoration: inverse filtering. 5.2 Homogeneous linear image restoration: Wiener filtering. 5.3 Homogeneous linear image restoration: Constrained matrix inversion. 5.4 Inhomogeneous linear image restoration: the whirl transform. 5.5 Nonlinear image restoration: MAP estimation. 5.6 Geometric image restoration. 6 Image Segmentation and Edge Detection. 6.1 Image segmentation. 6.2 Edge detection. 6.3 Phase congruency and the monogenic signal. 7 Image Processing for Multispectral Images. 7.1 Image preprocessing for multispectral images. 7.2 The physics and psychophysics of colour vision. 7.3 Colour image processing in practice. Bibliographical notes. References. Index.
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