{"title":"Pattern recognition","description":"Books on the subject of Pattern recognition","products":[{"product_id":"linear-algebra-and-learning-from-data-hardback-9780692196380","title":"Linear Algebra and Learning from Data (Hardback) 9780692196380","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eLinear Algebra and Learning from Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eFrom Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGilbert Strang (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780692196380, Wellesley-Cambridge Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 31 January 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e446 pages\u003cbr\u003e24.2 x 19.6 x 2.5 cm, 0.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eLinear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eDeep learning and neural nets\u003cbr\u003e Preface and acknowledgements\u003cbr\u003e Part I. Highlights of Linear Algebra\u003cbr\u003e Part II. Computations with Large Matrices\u003cbr\u003e Part III. Low Rank and Compressed Sensing\u003cbr\u003e Part IV. Special Matrices\u003cbr\u003e Part V. Probability and Statistics\u003cbr\u003e Part VI. Optimization\u003cbr\u003e Part VII. Learning from Data: Books on machine learning\u003cbr\u003e Eigenvalues and singular values\u003cbr\u003e Rank One\u003cbr\u003e Codes and algorithms for numerical linear algebra\u003cbr\u003e Counting parameters in the basic factorizations\u003cbr\u003e Index of authors\u003cbr\u003e Index\u003cbr\u003e Index of symbols.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Maths for computer scientists [\u003ca title=\"See our other books on Maths for computer scientists\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Maths%20for%20computer%20scientists%20%5BUYAM%5D%22\"\u003eUYAM\u003c\/a\u003e], Mathematical modelling [\u003ca title=\"See our other books on Mathematical modelling\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematical%20modelling%20%5BPBWH%5D%22\"\u003ePBWH\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wellesley-Cambridge Press","offers":[{"title":"Default Title","offer_id":45999586640152,"sku":"9780692196380","price":60.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780692196380i_aa4a255b-2aa8-4513-a20b-c00e8c8fcd43.jpg?v=1691359193"},{"product_id":"high-dimensional-probability-an-introduction-with-applications-in-data-science-hardback-9781108415194","title":"High-Dimensional Probability; An Introduction with Applications in Data Science (Hardback) 9781108415194","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHigh-Dimensional Probability\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAn Introduction with Applications in Data Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eAn integrated package of powerful probabilistic tools and key applications in modern mathematical data science.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRoman Vershynin (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781108415194, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 September 2018\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e296 pages\u003cbr\u003e26 x 18.3 x 2.2 cm, 0.71 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'A good textbook is as much about learning as about learning something specific. Vershynin's High-Dimensional Probability is a good textbook. When developing a topic, it starts from the simplest idea, it examines its weaknesses and builds up to a better idea; this is superbly done when bounding the tail probabilities of binomial distributions in Chapter 2. It always prioritises high-level narrative to technical details; the reader never loses sight of the main theme, arguments are kept to their essence, side results are given as exercises and important special cases are given priority over the most general statements. Intuition is at least as important as the techniques; this is usually the hardest to communicate in a book, compared for example to a classroom presentation, but it comes across beautifully in this book …' Omiros Papaspiliopoulos, Newsletter of the Bachelier Finance Society\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eHigh-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Appetizer: using probability to cover a geometric set\u003cbr\u003e 1. Preliminaries on random variables\u003cbr\u003e 2. Concentration of sums of independent random variables\u003cbr\u003e 3. Random vectors in high dimensions\u003cbr\u003e 4. Random matrices\u003cbr\u003e 5. Concentration without independence\u003cbr\u003e 6. Quadratic forms, symmetrization and contraction\u003cbr\u003e 7. Random processes\u003cbr\u003e 8. Chaining\u003cbr\u003e 9. Deviations of random matrices and geometric consequences\u003cbr\u003e 10. Sparse recovery\u003cbr\u003e 11. Dvoretzky-Milman's theorem\u003cbr\u003e Bibliography\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\u003c\/a\u003e], Econometrics [\u003ca title=\"See our other books on Econometrics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Econometrics%20%5BKCH%5D%22\"\u003eKCH\u003c\/a\u003e], Data analysis: general [\u003ca title=\"See our other books on Data analysis: general\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Data%20analysis:%20general%20%5BGPH%5D%22\"\u003eGPH\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46000401318168,"sku":"9781108415194","price":46.66,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781108415194i.jpg?v=1696753843"},{"product_id":"inference-and-learning-from-data-9781009218108","title":"Inference and Learning from Data (Multiple-component retail product) 9781009218108","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eInference and Learning from Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eDiscover core topics in inference and learning with this extraordinary three-volume set.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli H. Sayed (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009218108, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eMultiple-component retail product, published 22 December 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e3370 pages\u003cbr\u003e25.5 x 18 x 12 cm, 5.42 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods. A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code. Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eVolume I. Foundations: 1. Matrix theory\u003cbr\u003e 2. Vector differentiation\u003cbr\u003e 3. Random variables\u003cbr\u003e 4. Gaussian distribution\u003cbr\u003e 5. Exponential distributions\u003cbr\u003e 6. Entropy and divergence\u003cbr\u003e 7. Random processes\u003cbr\u003e 8. Convex functions\u003cbr\u003e 9. Convex optimization\u003cbr\u003e 10. Lipschitz conditions\u003cbr\u003e 11. Proximal operator\u003cbr\u003e 12. Gradient descent method\u003cbr\u003e 13. Conjugate gradient method\u003cbr\u003e 14. Subgradient method\u003cbr\u003e 15. Proximal and mirror descent methods\u003cbr\u003e 16. Stochastic optimization\u003cbr\u003e 17. Adaptive gradient methods\u003cbr\u003e 18. Gradient noise\u003cbr\u003e 19. Convergence analysis I: stochastic gradient algorithms\u003cbr\u003e 20. Convergence analysis II: stochasic subgradient algorithms\u003cbr\u003e 21. Convergence analysis III: stochastic proximal algorithms\u003cbr\u003e 22. Variance-reduced methods I: uniform sampling\u003cbr\u003e 23. Variance-reduced methods II: random reshuffling\u003cbr\u003e 24. Nonconvex optimization\u003cbr\u003e 25. Decentralized optimization I: primal methods\u003cbr\u003e 26. Decentralized optimization II: primal-dual methods\u003cbr\u003e Author index\u003cbr\u003e Subject index. Volume II. Inference: 27. Mean-Square-Error inference\u003cbr\u003e 28. Bayesian inference\u003cbr\u003e 29. Linear regression\u003cbr\u003e 30. Kalman filter\u003cbr\u003e 31. Maximum likelihood\u003cbr\u003e 32. Expectation maximization\u003cbr\u003e 33. Predictive modeling\u003cbr\u003e 34. Expectation propagation\u003cbr\u003e 35. Particle filters\u003cbr\u003e 36. Variational inference\u003cbr\u003e 37. Latent Dirichlet allocation\u003cbr\u003e 38. Hidden Markov models\u003cbr\u003e 39. Decoding HMMs\u003cbr\u003e 40. Independent component analysis\u003cbr\u003e 41. Bayesian networks\u003cbr\u003e 42. Inference over graphs\u003cbr\u003e 43. Undirected graphs\u003cbr\u003e 44. Markov decision processes\u003cbr\u003e 45. Value and policy iterations\u003cbr\u003e 46. Temporal difference learning\u003cbr\u003e 47. Q-learning\u003cbr\u003e 48. Value function approximation\u003cbr\u003e 49. Policy gradient methods\u003cbr\u003e Author index\u003cbr\u003e Subject index. Volume III. Learning: 50. Least-squares problems\u003cbr\u003e 51. Regularization\u003cbr\u003e 52. Nearest-neighbor rule\u003cbr\u003e 53. Self-organizing maps\u003cbr\u003e 54. Decision trees\u003cbr\u003e 55. Naive Bayes classifier\u003cbr\u003e 56. Linear discriminant analysis\u003cbr\u003e 57. Principal component analysis\u003cbr\u003e 58. Dictionary learning\u003cbr\u003e 59. Logistic regression\u003cbr\u003e 60. Perceptron\u003cbr\u003e 61. Support vector machines\u003cbr\u003e 62. Bagging and boosting\u003cbr\u003e 63. Kernel methods\u003cbr\u003e 64. Generalization theory\u003cbr\u003e 65. Feed forward neural networks\u003cbr\u003e 66. Deep belief networks\u003cbr\u003e 67. Convolutional networks\u003cbr\u003e 68. Generative networks\u003cbr\u003e 69. Recurrent networks\u003cbr\u003e 70. Explainable learning\u003cbr\u003e 71. Adversarial attacks\u003cbr\u003e 72. Meta learning\u003cbr\u003e Author index\u003cbr\u003e Subject index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Communications engineering \/ telecommunications [\u003ca title=\"See our other books on Communications engineering \/ telecommunications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Communications%20engineering%20\/%20telecommunications%20%5BTJK%5D%22\"\u003eTJK\u003c\/a\u003e], Information theory [\u003ca title=\"See our other books on Information theory\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20theory%20%5BGPF%5D%22\"\u003eGPF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46002486542616,"sku":"9781009218108","price":167.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781009218108i.jpg?v=1696791451"},{"product_id":"dna-words-and-models-statistics-of-exceptional-words-hardback-9780521847292","title":"DNA, Words and Models; Statistics of Exceptional Words (Hardback) 9780521847292","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDNA, Words and Models\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eStatistics of Exceptional Words\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eThis book introduces mathematical and statistical ideas used for pattern analysis in computational biology.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eS. Robin (Author), F. Rodolphe (Author), S. Schbath (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521847292, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 October 2005\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e158 pages, 28 b\/w illus.  14 tables\u003cbr\u003e23.5 x 15.6 x 1.6 cm, 0.391 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'For statisticians with a little background in biology, this book delivers a very readable presentation on the analysis of DNA sequences to determine whether a motif is of statistical significance due to its overabundance (or under-abundance) in terms of frequencies or location. This book is concise but sufficiently detailed. Biologists without a background in mathematical statistics may find the learning curve a little steep but tractable. The authors' continuous use of practical examples will be greatly appreciate by biologists and statisticians alike. This book is one of a kind, and I recommend it to any statistician interested in learning about DNA sequences and motifs.' Journal of the American Statistical Association\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eAn important problem in computational biology is identifying short DNA sequences (mathematically, 'words') associated to a biological function. One approach consists in determining whether a particular word is simply random or is of statistical significance, for example, because of its frequency or location. This book introduces the mathematical and statistical ideas used in solving this so-called exceptional word problem. It begins with a detailed description of the principal models used in sequence analysis: Markovian models are central here and capture compositional information on the sequence being analysed. There follows an introduction to several statistical methods that are used for finding exceptional words with respect to the model used. The second half of the book is illustrated with numerous examples provided from the analysis of bacterial genomes, making this a practical guide for users facing a real situation and needing to make an adequate procedure choice.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eIntroduction\u003cbr\u003e 1. Simple models for biological sequences\u003cbr\u003e 2. Introduction to Markov chain models\u003cbr\u003e 3. Taking heterogeneities into account\u003cbr\u003e 4. Statistical properties of word occurrences\u003cbr\u003e 5. Words with unexpected frequencies\u003cbr\u003e 6. Words with unexpected locations.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Molecular biology [\u003ca title=\"See our other books on Molecular biology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Molecular%20biology%20%5BPSD%5D%22\"\u003ePSD\u003c\/a\u003e], DNA \u0026amp; Genome [\u003ca title=\"See our other books on DNA \u0026amp; Genome\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22DNA%20\u0026amp;%20Genome%20%5BPSAK1%5D%22\"\u003ePSAK1\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46265235833112,"sku":"9780521847292","price":70.36,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521847292i.jpg?v=1692020864"},{"product_id":"visual-computing-for-medicine-theory-algorithms-and-applications-hardback-9780124158733","title":"Visual Computing for Medicine; Theory, Algorithms, and Applications (Hardback) 9780124158733","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eVisual Computing for Medicine\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory, Algorithms, and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eFully revamped and updated to broaden the scope from pure visualization to visual computing, this new edition of a key book by medical visualization luminaries offers cutting-edge visualization techniques and applications for medical diagnosis and treatment. \u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eBernhard Preim (Author), Charl P Botha (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780124158733, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 26 November 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e836 pages, Approx. 430 illustrations (430 in full color)\u003cbr\u003e23.4 x 19 x 4.2 cm, 1.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\" I highly recommend it for one-semester advanced graduate courses in computer graphics. For graduate students pursuing PhDs and professionals in research and development in the medical visualization filed, this book is well worth reading.\" \u003cb\u003e--Computing Reviews, November 2014\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eVisual Computing for Medicine, Second Edition, \u003c\/i\u003eoffers cutting-edge visualization techniques and their applications in medical diagnosis, education, and treatment. The book includes algorithms, applications, and ideas on achieving reliability of results and clinical evaluation of the techniques covered. Preim and Botha illustrate visualization techniques from research, but also cover the information required to solve practical clinical problems. They base the book on several years of combined teaching and research experience. This new edition includes six new chapters on treatment planning, guidance and training; an updated appendix on software support for visual computing for medicine; and a new global structure that better classifies and explains the major lines of work in the field. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Acquisition, Analysis, and Interpretation of Medical Volume Data\u003c\/b\u003e2. Acquisition of Medical Image Data3. An Introduction to Medical Visualization in Clinical Practice4. Image Analysis for Medical Visualization5. Human-Computer Interaction for Medical Visualization\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Visualization and Exploration of Medical Volume Data\u003c\/b\u003e6. Surface Rendering7. Direct Volume Visualization8. Advanced Direct Volume Visualization 9. Volume Interaction10. Labeling and Measurements in Medical Visualization\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Advanced Medical Visualization Techniques\u003c\/b\u003e11. Visualization of Vascular Structures12. Illustrative Medical Visualization13. Virtual Endoscopy14. Projections and Reformations ONLINE\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Visualization of High-Dimensional Medical Image Data\u003c\/b\u003eVisualization of Brain Connectivity15. Visual Exploration and Analysis of Perfusion Data - ONLINE\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Treatment planning, guidance and training\u003c\/b\u003eComputer-Assisted Surgery16. Image-Guided Surgery and Augmented Reality17. Visual Exploration of Simulated and Measured Flow Data18. Visual Computing for ENT Surgery Planning - ONLINE19. Computer-Assisted Medical Education - ONLINE20. Outlook - ONLINE\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Graphical \u0026amp; digital media applications [\u003ca title=\"See our other books on Graphical \u0026amp; digital media applications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Graphical%20\u0026amp;%20digital%20media%20applications%20%5BUG%5D%22\"\u003eUG\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648325046552,"sku":"9780124158733","price":63.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780124158733.jpg?v=1694090633"},{"product_id":"introduction-to-pattern-recognition-a-matlab-approach-paperback-9780123744869","title":"Introduction to Pattern Recognition; A Matlab Approach (Paperback \/ softback) 9780123744869","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntroduction to Pattern Recognition\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Matlab Approach\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eAn accompanying manual to Theodoridis\/Koutroumbas, Pattern Recognition, that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSergios Theodoridis (Author), Aggelos Pikrakis (Author), Konstantinos Koutroumbas (Author), Dionisis Cavouras (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780123744869, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 30 April 2010\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e240 pages\u003cbr\u003e23.4 x 19 x 1.6 cm, 0.43 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eIntroduction to Pattern Recognition: A Matlab Approach\u003c\/i\u003e is an accompanying manual to Theodoridis\/Koutroumbas' Pattern Recognition.\u003c\/p\u003e  \u003cp\u003eIt includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.\u003c\/p\u003e  \u003cp\u003eThis text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R\u0026amp;D engineers and university researchers in image and signal processing\/analyisis, and computer vision. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface Chapter 1. Classifiers Based on Bayes Decision Theory     1.1 Introduction     1.2 Bayes Decision Theory     1.3 The Gaussian Probability Density Function     1.4 Minimum Distance Classifiers          1.4.1 The Euclidean Distance Classifier          1.4.2 The Mahalanobis Distance Classifier          1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs     1.5 Mixture Models     1.6 The Expectation-Maximization Algorithm     1.7 Parzen Windows     1.8 k-Nearest Neighbor Density Estimation     1.9 The Naive Bayes Classifier     1.10 The Nearest Neighbor RuleChapter 2. Classifiers Based on Cost Function Optimization     2.1 Introduction     2.2 The Perceptron Algorithm     2.2.1 The Online Form of the Perceptron Algorithm     2.3 The Sum of Error Squares Classifier          2.3.1 The Multiclass LS Classifier     2.4 Support Vector Machines: The Linear Case          2.4.1 Multiclass Generalizations     2.5 SVM: The Nonlinear Case     2.6 The Kernel Perceptron Algorithm     2.7 The AdaBoost Algorithm     2.8 Multilayer PerceptronsChapter 3. Data Transformation: Feature Generation and Dimensionality Reduction     3.1 Introduction     3.2 Principal Component Analysis     3.3 The Singular Value Decomposition Method     3.4 Fisher's Linear Discriminant Analysis     3.5 The Kernel PCA     3.6 Laplacian EigenmapChapter 4. Feature Selection     4.1 Introduction     4.2 Outlier Removal     4.3 Data Normalization     4.4 Hypothesis Testing: The t-Test     4.5 The Receiver Operating Characteristic Curve     4.6 Fisher's Discriminant Ratio     4.7 Class Separability Measures          4.7.1 Divergence          4.7.2 Bhattacharyya Distance and Chernoff Bound          4.7.3 Measures Based on Scatter Matrices     4.8 Feature Subset Selection          4.8.1 Scalar Feature Selection          4.8.2 Feature Vector SelectionChapter 5. Template Matching     5.1 Introduction     5.2 The Edit Distance     5.3 Matching Sequences of Real Numbers     5.4 Dynamic Time Warping in Speech RecognitionChapter 6. Hidden Markov Models     6.1 Introduction     6.2 Modeling     6.3 Recognition and TrainingChapter 7. Clustering     7.1 Introduction     7.2 Basic Concepts and Definitions     7.3 Clustering Algorithms     7.4 Sequential Algorithms          7.4.1 BSAS Algorithm          7.4.2 Clustering Refinement     7.5 Cost Function Optimization Clustering Algorithms          7.5.1 Hard Clustering Algorithms          7.5.2 Nonhard Clustering Algorithms     7.6 Miscellaneous Clustering Algorithms     7.7 Hierarchical Clustering Algorithms          7.7.1 Generalized Agglomerative Scheme          7.7.2 Specific Agglomerative Clustering Algorithms          7.7.3 Choosing the Best ClusteringAppendixReferencesIndex\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Computer vision [\u003ca title=\"See our other books on Computer vision\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20vision%20%5BUYQV%5D%22\"\u003eUYQV\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Computer modelling \u0026amp; simulation [\u003ca title=\"See our other books on Computer modelling \u0026amp; simulation\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20modelling%20\u0026amp;%20simulation%20%5BUYM%5D%22\"\u003eUYM\u003c\/a\u003e], Communications engineering \/ telecommunications [\u003ca title=\"See our other books on Communications engineering \/ telecommunications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Communications%20engineering%20\/%20telecommunications%20%5BTJK%5D%22\"\u003eTJK\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46648479449368,"sku":"9780123744869","price":25.97,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780123744869_87b36595-a3b5-42ca-8a9d-0f44ce51fa2d.jpg?v=1695013908"},{"product_id":"plan-activity-and-intent-recognition-theory-and-practice-paperback-9780123985323","title":"Plan, Activity, and Intent Recognition; Theory and Practice (Paperback) 9780123985323","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePlan, Activity, and Intent Recognition\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Practice\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eGathers together core knowledge with the latest research and provides a single reference source for researchers\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGita Sukthankar (Edited by), Christopher Geib (Edited by), Hung Hai Bui (Edited by), David Pynadath (Edited by), Robert P. Goldman (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780123985323, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 10 April 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e424 pages\u003cbr\u003e23.4 x 19 x 2.7 cm, 0.97 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"This book serves to provide a coherent snapshot of the exciting developments in the field enabled by improved sensors, increased computational power, and new application areas.\" \u003cb\u003e- HPCMagazine.com, August 2014\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior. These inferences are crucial in a wide range of applications including intelligent assistants, computer security, and dialogue management systems. This volume, edited by leading researchers, provides a timely snapshot of some of the key formulations, techniques, and applications that have been developed in this rich and rapidly evolving field.\" \u003cb\u003e--Dr. Hector Geffner, ICREA \u0026amp; Universitat Pompeu Fabra, Barcelona\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"This book collects some of the top senior people in the field of plan recognition with some of the newest researchers. It offers a comprehensive review of plan recognition from multiple viewpoints, encompassing both logical and probabilistic formalisms and covering mathematical theory, computer science applications, and human cognitive models.\" \u003cb\u003e--Dr. Peter Norvig, Director of Research at Google Inc.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"Plan, Activity, and Intent Recognition is an indispensable resource for creating systems that infer peoples’ goals and plans on the basis of their behavior. Researchers in security, natural language dialog systems, smart spaces and pervasive computing, and other areas will find a comprehensive and up to date survey of methods, applications, and open research challenges.\" \u003cb\u003e--Dr. Henry Kautz, University of Rochester, Past President of AAAI (Association for the Advancement of Artificial Intelligence) \u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePlan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human\/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. \u003c\/p\u003e \u003ci\u003e  \u003c\/i\u003e\u003cp\u003ePlan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: \u003c\/p\u003e  \u003cul\u003e \u003cli\u003epersonal agent assistants \u003c\/li\u003e \u003cli\u003ecomputer and network security \u003c\/li\u003e \u003cli\u003eopponent modeling in games and simulation systems \u003c\/li\u003e \u003cli\u003ecoordination in robots and software agents \u003c\/li\u003e \u003cli\u003eweb e-commerce and collaborative filtering \u003c\/li\u003e \u003cli\u003edialog modeling \u003c\/li\u003e \u003cli\u003evideo surveillance \u003c\/li\u003e \u003cli\u003esmart homes \u003c\/li\u003e\n\u003c\/ul\u003e  \u003cp\u003eIn this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePlan and Goal Recognition\u003c\/b\u003e  1. Hierarchical Goal Recognition  2. Weighted Abduction for Discourse Processing Based on Integer Linear Programming  3. Plan Recognition using Statistical Relational Models  4. Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior \u003c\/p\u003e \u003cp\u003e\u003cb\u003eActivity Discovery and Recognition\u003c\/b\u003e  5. Scaling Activity Recognition  6. Extraction of Latent Patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes \u003c\/p\u003e \u003cp\u003e\u003cb\u003eModeling Human Cognition  \u003c\/b\u003e7. Modeling Human Plan Recognition using Bayesian Theory of Mind  8. Decision Theoretic Planning in Multiagent Settings with Application to Modeling Human Strategic Behavior \u003c\/p\u003e \u003cp\u003e\u003cb\u003eMultiagent Systems\u003c\/b\u003e  9. Multiagent Plan Recognition from Partially Observed Team Traces  10. Role-based Ad Hoc Teamwork \u003c\/p\u003e \u003cp\u003e\u003cb\u003eApplications\u003c\/b\u003e  11. Probabilistic plan recognition for proactive assistant agents  12. Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks  13. Using Opponent Modeling to Adapt Team Play in American Football   14. Intent Recognition for Human-Robot Interaction  \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Human-computer interaction [\u003ca title=\"See our other books on Human-computer interaction\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Human-computer%20interaction%20%5BUYZ%5D%22\"\u003eUYZ\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46649337905432,"sku":"9780123985323","price":79.69,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780123985323.jpg?v=1694099818"},{"product_id":"artificial-vision-image-description-recognition-and-communication-hardback-9780124448162","title":"Artificial Vision; Image Description, Recognition, and Communication (Hardback) 9780124448162","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Vision\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eImage Description, Recognition, and Communication\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eStefano Levialdi (Edited by), Virginio Cantoni (Edited by), Vito Roberto (Edited by), Edward J. Powers (Series edited by), Doug Gray (Series edited by), Richard C. Green (Series edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780124448162, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 September 1996\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e306 pages\u003cbr\u003e22.9 x 15.2 x 2.3 cm, 0.57 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eArtificial Vision is a rapidly growing discipline, aiming to build computational models of the visual functionalities in humans, as well as machines that emulate them. Visual communication in itself involves a numberof challenging topics with a dramatic impact on contemporary culture where human-computer interaction and human dialogue play a more and more significant role.\u003c\/p\u003e  \u003cp\u003eThis state-of-the-art book brings together carefully selected review articles from world renowned researchers at the forefront of this exciting area. The contributions cover topics including image processing, computational geometry, optics, pattern recognition, and computer science. The book is divided into three sections. Part I covers active vision; Part II deals with the integration of visual with cognitive capabilities; and Part III concerns visual communication.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eArtificial Vision\u003c\/i\u003e will be essential reading for students and researchers in image processing, vision, and computer science who want to grasp the current concepts and future directions of this challenging field.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface. \u003cb\u003ePart I: Active Vision:\u003c\/b\u003e \u003ci\u003eV. Cantoni, G. Caputo, and L. Lombardi,\u003c\/i\u003e Attentional Engagement in Vision Systems. \u003ci\u003eY. Yeshurun,\u003c\/i\u003e Attentional Mechanisms in Computer Vision. \u003ci\u003eM. Savini,\u003c\/i\u003e The Active Vision Idea. \u003ci\u003eE. Trucco,\u003c\/i\u003e Active Model Acquisition and Sensor Planning. \u003ci\u003eA. Verri,\u003c\/i\u003e The Regularization of Early Vision. \u003cb\u003ePart II: Integrating Visual Modules:\u003c\/b\u003e \u003ci\u003eM. Bertolotto, E. Bruzzone, and L. DeFloriani,\u003c\/i\u003e Geometric Modeling and Spatial Reasoning.\u003ci\u003eV. Roberto,\u003c\/i\u003e Vision as Uncertain Knowledge. \u003ci\u003eV. Di Gesu and D. Tegolo,\u003c\/i\u003e Distributed Systems for Fusion of Visual Infomation. \u003ci\u003eE. Ardizzone, A. Chella, and S. Gaglio,\u003c\/i\u003e Hybrid Computation and Reasoning for Artificial Vision. \u003cb\u003ePart III: VisualCommunication:\u003c\/b\u003e \u003ci\u003eA.M. Iacono,\u003c\/i\u003e Illusion and Difference. \u003ci\u003eS. Levialdi, P. Mussio, N. Bianchi, and P. Bottoni,\u003c\/i\u003e Computing with\/on Images. \u003ci\u003eA. Del Bimbo,\u003c\/i\u003e Visual Databases. \u003ci\u003eS-K. Chang,\u003c\/i\u003e Visual Languages for Tele-Action Objects. Subject Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e], Computer vision [\u003ca title=\"See our other books on Computer vision\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20vision%20%5BUYQV%5D%22\"\u003eUYQV\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Robotics [\u003ca title=\"See our other books on Robotics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Robotics%20%5BTJFM1%5D%22\"\u003eTJFM1\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46649420644632,"sku":"9780124448162","price":57.88,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780124448162.jpg?v=1694100878"}],"url":"https:\/\/freshlyprintedbooks.co.uk\/collections\/pattern-recognition.oembed","provider":"Freshly Printed Books","version":"1.0","type":"link"}