Image Processing: The FundamentalsFollowing 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. Key features:
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 |
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algorithm apply assume autocorrelation function autocovariance average axes axis band basis images calculate coeﬃcients colour system column components compute conﬁguration consider convolution corresponding cost function create deﬁned derivative diagonal diﬀerent discrete Fourier transform edge edge detection eﬀect eigenvalues eigenvectors elementary images elements enhancement ensemble equation error for image example ﬁeld Figure ﬁlter ﬁnd ﬁrst ﬂat formula frequency Gaussian given gradient magnitude grey levels grey value histogram identiﬁed identify image g image processing integration inverse iterations linear matrix H mean multiply negentropy neighbours noise normalised obtain original image orthogonal outer product output parameters pixel point spread function position probability density function produce random field random variables range reconstruction reﬂectance function result samples sensor shown in ﬁgure shows signal spatial spectra spectrum Square error Step symmetric take values threshold uncorrelated vector Walsh functions wavelengths