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Graph partitioning is a theoretical subject with applications in many areas, principally: numerical analysis, programs mapping onto parallel architectures, image segmentation, VLSI design. During the last 40 years, the literature has strongly increased and big improvements have b...een made.
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Filling the gap between introductory and encyclopedic treatments, this book provides rich and appealing material for a second course in combinatorial optimization. This book is suitable for graduate students as well as a reference for established researchers.
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This text, presented in three parts (Combinatorics, Probability, Graphs) addresses all those who wish to acquire basic or advanced knowledge in combinatorial theories. Basic and advanced theoretical elements are presented through simple applications like the Sudoku game, search e...ngine algorithm and other easy to grasp applications.
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Concerns the use of graphs for the simulation and representation of transport networks. This book covers networks in both spatial analysis and urban management, together with the simulation by graph theory, a tool that makes it possible to solve various classic problems, such as ...high-speed roads between one or more origins and destinations.
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Published with the contribution of the Faculty of Letters and Philosoophy of the Unversity of Calabria.
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Offers a blend, which is shown to be useful for a variety of applications, including robotics, geometrical optics, computer animation, and geometric design.
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Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporat...ing both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.
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Serious mathematics treated with levity --Title page.
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Suitable for upper-level undergraduates and graduate students in engineering, science, and mathematics, this introductory text explores counting and listing, graphs, induction and recursion, and generating functions. Includes numerous exercises (some with solutions), notes, and r...eferences.
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An introduction to basic counting techniques for upper secondary to undergraduate students, as well as teachers. It helps to give students an early start to learning problem-solving heuristics and thinking skills. It also includes chapters such as the Principle of Inclusion and E...xclusion, the Pigeonhole Principle, and, the Catalan Numbers.
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