Covers some of the most significant applications of artificial intelligence, namely: natural language processing, speech understanding, expert system design, requirement engineering, machine learning, truth maintenance systems, advanced concepts and methods of logic programming. Together with the previous two volumes edited by Thayse, this completes a comprehensive exposition of the subject of logics applied to AI.

From Natural Language Processing to Logic for Expert Systems: A Logic Based Approach to Artificial Intelligence

van Lamsweerde, Axel

Kamp, Yves

Snyers, Dominique

Van Der Linden, Frank

DUPONT, PIERRE

Dubois, Eric

Binot, Jean-Louis

Bruffaerts, Albert

Delsarte, Philippe

Hagelstein, Jacques

Henin, Eric

Thayse, André

Müller, Jean-Pierre

Rifaut, André

Computers & Technology

wiley

An Innovative Approach to Multidimensional Signals and Systems Theory for Image and Video Processing In this volume, Eric Dubois further develops the theory of multi-D signal processing wherein input and output are vector-value signals. With this framework, he introduces the reader to crucial concepts in signal processing such as continuous- and discrete-domain signals and systems, discrete-domain periodic signals, sampling and reconstruction, light and color, random field models, image representation and more. While most treatments use normalized representations for non-rectangular sampling, this approach obscures much of the geometrical and scale information of the signal. In contrast, Dr. Dubois uses actual units of space-time and frequency. Basis-independent representations appear as much as possible, and the basis is introduced where needed to perform calculations or implementations. Thus, lattice theory is developed from the beginning and rectangular sampling is treated as a special case. This is especially significant in the treatment of color and color image processing and for discrete transform representations based on symmetry groups, including fast computational algorithms. Other features include: <ul>
<li>An entire chapter on lattices, giving the reader a thorough grounding in the use of lattices in signal processing</li> <li>Extensive treatment of lattices as used to describe discrete-domain signals and signal periodicities</li> <li>Chapters on sampling and reconstruction, random field models, symmetry invariant signals and systems and multidimensional Fourier transformation properties</li> <li>Supplemented throughout with MATLAB examples and accompanying downloadable source code</li> </ul>
Graduate and doctoral students as well as senior undergraduates and professionals working in signal processing or video/image processing and imaging will appreciate this fresh approach to multidimensional signals and systems theory, both as a thorough introduction to the subject and as inspiration for future research.

Multidimensional Signal and Color Image Processing Using Lattices

Networking & Cloud Computing

This lecture describes the author's approach to the representation of color spaces and their use for color image processing. The lecture starts with a precise formulation of the space of physical stimuli (light). The model includes both continuous spectra and monochromatic spectra in the form of Dirac deltas. The spectral densities are considered to be functions of a continuous wavelength variable. This leads into the formulation of color space as a three-dimensional vector space, with all the associated structure. The approach is to start with the axioms of color matching for normal human viewers, often called Grassmann's laws, and developing the resulting vector space formulation. However, once the essential defining element of this vector space is identified, it can be extended to other color spaces, perhaps for different creatures and devices, and dimensions other than three. The CIE spaces are presented as main examples of color spaces. Many properties of the color space are examined. Once the vector space formulation is established, various useful decompositions of the space can be established. The first such decomposition is based on luminance, a measure of the relative brightness of a color. This leads to a direct-sum decomposition of color space where a two-dimensional subspace identifies the chromatic attribute, and a third coordinate provides the luminance. A different decomposition involving a projective space of chromaticity classes is then presented. Finally, it is shown how the three types of color deficiencies present in some groups of humans leads to a direct-sum decomposition of three one-dimensional subspaces that are associated with the three types of cone photoreceptors in the human retina. Next, a few specific linear and nonlinear color representations are presented. The color spaces of two digital cameras are also described. Then the issue of transformations between \emph{different} color spaces is addressed.

The Structure and Properties of Color Spaces and the Representation of Color Images (Synthesis Lectures on Image, Video, and Multimedia Processing, 11)

Computer Science

Morgan & Claypool Publishers

Vague à l'âme: Poèmes 1998-2001 (French Edition)

LE MANUSCRIT

The Structure and Properties of Color Spaces and the Representation of Color Images (Synthesis Lectures on Image, Video, and Multimedia Processing)

Springer

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