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)
Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.
4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings
Author: Petra Perner,Atsushi Imiya
Publisher: Springer Science & Business Media
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.
Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community. * The latest results on support vector machines including v-SVM's and their geometric interpretation * Classifier combinations including the Boosting approach * State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics * Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
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. There are six chapters on current activities in human identification. Other topics include moving object tracking, performance evaluation, content-based video analysis, musical style recognition, number plate recognition, etc. Contents: Basic Methods in Pattern RecognitionBasic Methods in Computer VisionRecognition ApplicationsHuman IdentificationSystem and Technology Readership: Graduate students, academics, practitioners, researchers, computer scientists, electrical and medical engineers. Keywords:Pattern Recognition;Computer Vision;Image Segmentation;Document Analysis and Understanding;Human Identification;Face Recognition;Multiple Object Tracking;Range Image AnalysisKey Features:Offers an in-depth treatment of the theory and applications of pattern recognition and computer visionCaptures the latest development in human identificationTackles a number of emerging topics in the field
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.
Schärfen Sie Ihren Blick für Schlüsselzüge im Schach
Author: International Master Arthur van de Oudeweetering
Publisher: New In Chess
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!
Edwin Hancock,Mario Vento,England) Gbrpr 200 (2003 York
4th IAPR International Workshop, GbRPR 2003, York, UK, June 30 - July 2, 2003. Proceedings
Author: Edwin Hancock,Mario Vento,England) Gbrpr 200 (2003 York
Publisher: Springer Science & Business Media
This volume contains the papers presented at the Fourth IAPR Workshop on Graph Based Representations in Pattern Recognition. The workshop was held at the King’s Manor in York, England between 30 June and 2nd July 2003. The previous workshops in the series were held in Lyon, France (1997), Haindorf, Austria (1999), and Ischia, Italy (2001). The city of York provided an interesting venue for the meeting. It has been said that the history of York is the history of England. There have been both Roman and Viking episodes. For instance, Constantine was proclaimed emperor in York. The city has also been a major seat of ecclesiastical power and was also involved in the development of the railways in the nineteenth century. Much of York’s history is evidenced by its buildings, and the King’s Manor is one of the most important and attractive of these. Originally part of the Abbey, after the dissolution of the monasteries by Henry VIII, the building became a center of government for the Tudors and the Stuarts (who stayed here regularly on their journeys between London and Edinburgh), serving as the headquarters of the Council of the North until it was disbanded in 1561. The building became part of the University of York at its foundation in 1963. The papers in the workshop span the topics of representation, segmentation, graph-matching, graph edit-distance, matrix and spectral methods, and gra- clustering.
Maschinelles Lernen heißt, Computer so zu programmieren, dass ein bestimmtes Leistungskriterium anhand von Beispieldaten und Erfahrungswerten aus der Vergangenheit optimiert wird. Das vorliegende Buch diskutiert diverse Methoden, die ihre Grundlagen in verschiedenen Themenfeldern haben: Statistik, Mustererkennung, neuronale Netze, Künstliche Intelligenz, Signalverarbeitung, Steuerung und Data Mining. In der Vergangenheit verfolgten Forscher verschiedene Wege mit unterschiedlichen Schwerpunkten. Das Anliegen dieses Buches ist es, all diese unterschiedlichen Ansätze zu kombinieren, um eine allumfassende Behandlung der Probleme und ihrer vorgeschlagenen Lösungen zu geben.
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.
Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.
Unentbehrlich für den chirurgischen Alltag! Ob zum Nachschlagen oder zum schnellen Abklären aktueller Probleme - "Fossum" lässt keine Fragen offen. Über 1.500 farbige Abbildungen verdeutlichen die Inhalte. Neu in der 2. Auflage • Neue Kapitel: physikalische Therapie, minimalinvasive Verfahren, Operationen des Auges • Deutlich erweitert:Perioperative multimodale Schmerztherapie, Arthroskopie, Ellenbogendysplasie beim Hund, Gelenkersatz und die Behandlung von Osteoarthritis • Mehr über die neuesten bildgebenden Verfahren
Dieser Buchtitel ist Teil des Digitalisierungsprojekts Springer Book Archives mit Publikationen, die seit den Anfängen des Verlags von 1842 erschienen sind. Der Verlag stellt mit diesem Archiv Quellen für die historische wie auch die disziplingeschichtliche Forschung zur Verfügung, die jeweils im historischen Kontext betrachtet werden müssen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen Ausrichtung vom Verlag nicht beworben.
4th International Workshop, EMMCVPR 2003, Lisbon, Portugal, July 7-9, 2003, Proceedings
Author: Anand Rangarajan,Mário Figueiredo
Publisher: Springer Science & Business Media
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.
Pulse-coupled neural networks; A neural network model for optical flow computation; Temporal pattern matching using an artificial neural network; Patterns of dynamic activity and timing in neural network processing; A macroscopic model of oscillation in ensembles of inhibitory and excitatory neurons; Finite state machines and recurrent neural networks: automata and dynamical systems approaches; biased random-waldk learning; a neurobiological correlate to trial-and-error; Using SONNET 1 to segment continuous sequences of items; On the use of high-level petri nets in the modeling of biological neural networks; Locally recurrent networks: the gmma operator, properties, and extensions.
Faced with a single neuroradiological image of an unknown patient, how confident would you be to make a differential diagnosis? Despite advanced imaging techniques, a confident diagnosis also requires knowledge of the patient's age, clinical data and the lesion location. Pattern Recognition Neuroradiology provides the tools you will need to arrive at the correct diagnosis or a reasonable differential diagnosis. This user-friendly book includes basic information often omitted from other texts: a practical method of image analysis, sample dictation templates and didactic information regarding lesions/diseases in a concise outline form. Image galleries show more than 700 high quality representative examples of the diseases discussed. Whether you are a trainee encountering some of these conditions for the first time or a resident trying to develop a reliable system of image analysis, Pattern Recognition Neuroradiology is an invaluable diagnostic resource.