Издательство ISTE Ltd / John Wiley, 2009, -385 pp.Engineers constantly encounter technological problems which are becoming increasingly complex. These problems may be encountered in different domains such as transport, telecommunications, genomics, technology for the healthcare sector and electronics. The given problem can often be expressed as one which could be solved by optimization. Within this process of optimization, one or several objective functions are defined. The aim of this process is to minimize the objective function in relation to all parameters concerned. Apart from problems of optimization, i.e. the problem’s objective function which is part of this topic (e.g. improving the shape of a ship, reducing polluting emissions, obtaining a maximum profit), a large number of other situations of indirect optimization can be encountered (e.g. identification of a model or the learning process of a new cognitive system). When looking at this issue from the angle of available methods used to resolve a given problem, a large variety of methods can be considered. On the one hand, there are classic methods that rely purely on mathematics, but impose strict application conditions. On the other hand, digital methods that could be referred to as heuristic do not try to find an ideal solution but try to obtain a solution in a given time available for the calculation. Part of the latter group of methods is metaheuristics, which emerged in the 1980s. Metaheuristics has many similarities with physics, biology or even ethology. Metaheuristics can be applied to a large variety of problems. Success can, however, not be guaranteed. The domain of optimization is also very interesting when it comes to its functions within the field of application. In the domain of optimization, the processing of signals and images is especially varied, which is due to its large number of different applications as well as the fact that it gave rise to specific theoretical approaches such as the Markov fields, to name just one example.
These ideas have influenced the title of this book Optimization in Signal and Image Processing. This book has been written for researchers, university lecturers and engineers working at research laboratories, universities or in the private sector. This book is also destined to be used in the education and training of PhD students as well as postgraduate and undergraduate students studying signal processing, applied mathematics and computer science. It studies some theoretical tools that are used in this field: artificial evolution and the Parisian approach, wavelets and fractals, information criteria, learning and quadratic programming, Bayesian formalism, probabilistic modeling, the Markovian approach, hidden Markov models and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms (EDA) and artificial immune systems). Theoretical approaches are illustrated by varied applications that are relevant to signals or images. Some examples include: analysis of 3D scenarios in robotics, detection of different aggregates in mammographic images, processing of hand-written numbers, tuning of sensors used in surveillance or exploration, underwater acoustic imagery, face recognition systems, detection of traffic signs, image registration of retinal angiography, estimation of physiological signals and tuning cochlear implants.
Because of the wide variety of different subjects, as well as their interdependence, it is impossible to structure this book – which contains 13 chapters
– into distinct divisions, which might, for example, separate traditional methods and metaheuristics, or create a distinction between methods dealing with signals or with imagery. However, it is possible to split these chapters into three main groups:
– the first group (Chapters 1 to 5) illustrates several general optimization tools related to signals and images;
– the second group (Chapters 6 to 10) consists of probabilistic, Markovian or Bayesian approaches;
– the third group (Chapters 11 to 13) describes applications that are relevant for engineering in the healthcare sector, which are dealt with here through the use of metaheuristics.
Modeling and Optimization in Image Analysis.
Artificial Evolution and the Parisian Approach. Applications in the Processing of Signals and Images.
Wavelets and Fractals for Signal and Image Analysis.
Information Criteria: Examples of Applications in Signal and Image Processing.
Quadratic Programming and Machine Learning – Large Scale Problems and Sparsity.
Probabilistic Modeling of Policies and Application to Optimal Sensor Management.
Optimizing Emissions for Tracking and Pursuit of Mobile Targets.
Bayesian Inference and Markov Models.
The Use of Hidden Markov Models for Image Recognition: Learning with Artificial Ants, Genetic Algorithms and Particle Swarm Optimization.
Biological Metaheuristics for Road Sign Detection.
Metaheuristics for Continuous Variables. The Registration of Retinal Angiogram Images.
Joint Estimation of the Dynamics and Shape of Physiological Signals through Genetic Algorithms.
Using Interactive Evolutionary Algorithms to Help Fit Cochlear Implants .
