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The volume Fuzzy Techniques in Image Processing illustrates the successful application of fuzzy set theory in the area of image processing, and represents a broad, up-to-date and state-of-the-art coverage of diverse aspects related to fuzzy techniques in image processing. Besides an extensive state-of-the-art contribution on fuzzy mathematical morphology we present several contributions on a wide variety of topics, including fuzzy filtering, fuzzy image enhancement, fuzzy edge detection, fuzzy image segmentation, fuzzy processing of color images, and applications in medical imaging and robot vision. All chapters are written by outstanding experts in the field, and are characterized by their originality and high-quality standard. Therefore, the book will not only serve as an important reference for scientists and practitioners in this area, but also as an inspiration for newcoming researchers.

Artificial intelligence. Robotics. Simulation. Graphics --- Fuzzy logic. --- Image processing --- Soft computing. --- Digital techniques. --- Optical data processing. --- Artificial intelligence. --- Information technology. --- Business—Data processing. --- Image Processing and Computer Vision. --- Artificial Intelligence. --- IT in Business. --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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During the last three decades, interest has increased significantly in the representation and manipulation of imprecision and uncertainty. Perhaps the most important technique in this area concerns fuzzy logic or the logic of fuzziness initiated by L. A. Zadeh in 1965. Since then, fuzzy logic has been incorporated into many areas of fundamental science and into the applied sciences. More importantly, it has been successful in the areas of expert systems and fuzzy control. The main body of this book consists of so-called IF-THEN rules, on which experts express their knowledge with respect to a certain domain of expertise. Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN rules. This work gives special attention to fuzzy IF-THEN rules as they are being applied in computational intelligence. Included are theoretical developments and applications related to IF-THEN problems of propositional calculus, fuzzy predicate calculus, implementations of the generalized Modus Ponens, approximate reasoning, data mining and data transformation, techniques for complexity reduction, fuzzy linguistic modeling, large-scale application of fuzzy control, intelligent robotic control, and numerous other systems and practical applications. This book is an essential resource for engineers, mathematicians, and computer scientists working in fuzzy sets, soft computing, and of course, computational intelligence.

Expert systems (Computer science) --- Fuzzy systems --- Computational intelligence --- 681.3*I23 --- Intelligence, Computational --- Artificial intelligence --- Systems, Fuzzy --- System analysis --- Fuzzy logic --- Knowledge-based systems (Computer science) --- Systems, Expert (Computer science) --- Computer systems --- Soft computing --- Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence) --- Computational intelligence. --- Fuzzy systems. --- Expert systems (Computer science). --- 681.3*I23 Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence) --- Mathematical logic. --- Operations research. --- Decision making. --- Calculus of variations. --- Artificial intelligence. --- Mathematical Logic and Foundations. --- Operations Research/Decision Theory. --- Calculus of Variations and Optimal Control; Optimization. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Isoperimetrical problems --- Variations, Calculus of --- Maxima and minima --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Decision making

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Elektronen. --- Kristallen.

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This book provides a timely overview of topics in fuzzy mathematics. It lays the foundation for further research and applications in a broad range of areas. It contains break-through analysis on how results from the many variations and extensions of fuzzy set theory can be obtained from known results of traditional fuzzy set theory. The book contains not only theoretical results, but a wide range of applications in areas such as decision analysis, optimal allocation in possibilistics and mixed models, pattern classification, credibility measures, algorithms for modeling uncertain data, and numerical methods for solving fuzzy linear systems. The book offers an excellent reference for advanced undergraduate and graduate students in applied and theoretical fuzzy mathematics. Researchers and referees in fuzzy set theory will find the book to be of extreme value.

Fuzzy mathematics.