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Handbook Of Pattern Recognition And Computer Vision 5th Edition

Author: Chi-hau Chen
Publisher: World Scientific
ISBN: 9814656534
Size: 51.85 MB
Format: PDF, Mobi
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The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures. Recognition applications include character recognition and document analysis, detection of digital mammograms, remote sensing image fusion, and analysis of functional magnetic resonance imaging data, etc.

Introduction To Pattern Recognition

Author: Sergios Theodoridis
Publisher: Academic Press
ISBN: 9780080922751
Size: 51.96 MB
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Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It 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. This 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&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

Pattern Recognition

Author: Sergios Theodoridis
Publisher: Academic Press
ISBN: 9780080949123
Size: 76.65 MB
Format: PDF, ePub, Mobi
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This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with 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. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.

Machine Learning And Data Mining In Pattern Recognition

Author: Petra Perner
Publisher: Springer Science & Business Media
ISBN: 3540269231
Size: 80.16 MB
Format: PDF, Docs
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We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.

Applied Pattern Recognition

Author: Dietrich Paulus
Publisher: Springer Science & Business Media
ISBN: 9783528355586
Size: 13.13 MB
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This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 4th edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed.

Mustererkennung Im Mittelspiel

Author: International Master Arthur van de Oudeweetering
Publisher: New In Chess
ISBN: 9056916165
Size: 23.86 MB
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Die Mustererkennung ist eines der wichtigsten Werkzeuge bei der Verbesserung im Schach. Die Erkenntnis, dass die Stellung auf dem Brett Ähnlichkeiten mit etwas hat, was man bereits gesehen hat, erleichtert Ihnen, rasch den Gehalt der Stellung zu erfassen und die vielversprechendste Fortsetzung zu finden. Mustererkennung im Mittelspiel versorgt Sie mit einem reichhaltigen Schatz an wichtigen und doch leicht einzuprägenden Bausteinen für Ihr Schachwissen. In 40 kurzen, scharf umrissenen Kapiteln präsentiert der erfahrene Schachtrainer Arthur van de Oudeweetering hunderte Beispiele zu verblüffenden Mittelspielthemen. Um Ihr Verständnis zu testen, gibt es zu jedem Abschnitt Aufgaben. Nach der Arbeit mit diesem Buch wird sich Ihr Schachwissen ganz wie von selbst um die Kenntnis zahlreicher Stellungstypen, Bauernstrukturen und Figurenkonstellationen vermehrt haben. Im Ergebnis werden Sie den richtigen Zug häufiger und auch rascher finden!

Object Detection And Recognition In Digital Images

Author: Boguslaw Cyganek
Publisher: John Wiley & Sons
ISBN: 111861836X
Size: 23.71 MB
Format: PDF, ePub, Mobi
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Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

Pattern Recognition In Bioinformatics

Author: Visakan Kadirkamanathan
Publisher: Springer
ISBN: 3642040314
Size: 57.20 MB
Format: PDF, ePub, Mobi
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This book constitutes the refereed proceedings of the Fourth International Workshop on Pattern Recognition in Bioinformatics, PRIB 2009, held in Sheffield, UK, in September 2009. The 38 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics covered by these papers range from image analysis for biomedical data to systems biology. The conference aims at crating a focus for the development and application of pattern recognition techniques in the biological domain.

Energy Minimization Methods In Computer Vision And Pattern Recognition

Author: Anand Rangarajan
Publisher: Springer
ISBN: 3540450637
Size: 54.34 MB
Format: PDF, Kindle
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This book constitutes the refereed proceedings of the 4th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2003, held in Lisbon, Portugal in July 2003.The 33 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on unsupervised learning and matching, probabilistic modeling, segmentation and grouping, shape modeling, restoration and reconstruction, and graphs and graph-based methods.