There are already many computer vision textbooks, and it is reasonable to question the need for another. Let me explain why I chose to write this volume.
Computer vision is an engineering discipline; we are primarily motivated by the real-world concern of...
We have been very pleased, beyond our expectations, with the reception of
the first edition of this book. Bioinformatics, however, continues to evolve
very rapidly, hence the need for a new edition. In the past three years, fullgenome
sequencing has blossomed with the completion of the sequence of
the fly and the first draft of the...
Over the last few decades, energy minimization methods have become an established
paradigm to resolve a variety of challenges in the fields of computer vision
and pattern recognition. While traditional approaches to computer vision were
often based on a heuristic sequence of processing steps and merely allowed a
very limited...
This volume contains the papers presented at the 14th International Workshop
on Approximation Algorithms for Combinatorial Optimization Problems
(APPROX 2011) and the 15th International Workshop on Randomization and
Computation (RANDOM 2011), which took place concurrently in Princeton
University, USA, during August 17–19,...
Connections between the theory of hyperbolic manifolds and the theory of
automata are deeply interwoven in the history of mathematics of this century.
The use of symbol sequences to study dynamical systems originates in the
work of Kocbe [Koc27, Koe29] and Morse [Mor87j, who both used symbol
saliences to code geodesies on a...
Anticipatory optimization for dynamic decision making relies on a number of different
scientific disciplines. On a general level, the foundations of the field may be
localized at the intersection of operations research, computer science and decision
theory. Closer inspection reveals the important role of branches such as simulation,...
In theory, Bayesian statistics is very simple. The posterior is proportional to the prior
times likelihood. This gives the shape of the posterior, but it is not a density so it
cannot be used for inference. The exact scale factor needed to make this a density
can be found only in a few special cases. For other cases, the scale...
Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...
Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of...
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete...
The book covers the standard models and techniques used in decision making in organizations. The main emphasis of the book is on modeling business-related scenarios and the generation of decision alternatives. Fully solved examples from many areas are used to illustrate the main concepts without getting bogged down in technical details. The...
