This book describes various types of image patterns for image retrieval. All these patterns are texture dependent. Few image patterns such as Improved directional local extrema patterns, Local Quantized Extrema Patterns, Local Color Oppugnant Quantized Extrema Patterns and Local Mesh quantized extrema patterns are presented. Inter-relationships among the pixels of an image are used for feature extraction. In contrast to the existing patterns these patterns focus on local neighborhood of pixels to creates the feature vector. Evaluation metrics such as precision and recall are calculated after testing with standard databases i.e., Corel-1k, Corel-5k and MIT VisTex database. This book serves as a practical guide for students and researchers. -The text introduces two models of Directional local extrema patterns viz., Integration of color and directional local extrema patterns Integration of Gabor features and directional local extrema patterns. -Provides a framework to extract the features using quantization method -Discusses the local quantized extrema collected from two oppugnant color planes -Illustrates the mesh structure with the pixels at alternate positions.

The field of biometrics utilizes computer models of the physical and behavioral characteristics of human beings with a view to reliable personal identification. The human characteristics of interest include visual images, speech, and indeed anything which might help to uniquely identify the individual.The other side of the biometrics coin is biometric synthesis ? rendering biometric phenomena from their corresponding computer models. For example, we could generate a synthetic face from its corresponding computer model. Such a model could include muscular dynamics to model the full gamut of human emotions conveyed by facial expressions.This book is a collection of carefully selected papers presenting the fundamental theory and practice of various aspects of biometric data processing in the context of pattern recognition. The traditional task of biometric technologies ? human identification by analysis of biometric data ? is extended to include the new discipline of biometric synthesis.

During the last twenty years the problem of pattern recognition (specifically, image recognition) has been studied intensively by many investigators, yet it is far from being solved. The number of publications increases yearly, but all the experimental results-with the possible exception of some dealing with recognition of printed characters-report a probability of error significantly higher than that reported for the same images by humans. It is widely agreed that ideally the recognition problem could be thought of as a problem in testing statistical hypotheses. However, in most applications the immediate use of even the simplest statistical device runs head on into grave computational difficulties, which cannot be eliminated by recourse to general theory. We must accept the fact that it is impossible to build a universal machine which can learn an arbitrary classification of multidimensional signals. Therefore the solution of the recognition problem must be based on a priori postulates (concerning the sets of signals to be recognized) that will narrow the set of possible classifications, i.e., the set of decision functions. This notion can be taken as the methodological basis for the approach adopted in this book.

Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse applications methodologies.Coverage includes: - Optical character recognition - Speech classification - Medical imaging - Paper currency recognition - Classification reliability techniques - Sensor technology Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.

The two volume set LNCS 3686 and LNCS 3687 constitutes the refereed proceedings of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, held in Bath, UK in August 2005. The papers submitted to ICAPR 2005 were thoroughly reviewed by up to three referees per paper and less than 40% of the submitted papers were accepted. The first volume includes 73 contributions related to Pattern Recognition and Data Mining (which included papers from the tracks of pattern recognition methods, knowledge and learning, and data mining); topics addressed are pattern recognition, data mining, signal processing and OCR/ document analysis. The second volume contains 87 contributions related to Pattern Recognition and Image Analysis (which included papers from the applications track) and deals with security and surveillance, biometrics, image processing and medical imaging. It also contains papers from the Workshop on Pattern Recognition for Crime Prevention.

A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.

Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection; novel computer system architectures; proven algorithms for solutions to common roadblocks in data processing; computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net; detailed appendices with data sets illustrating key concepts in the text; and more.

This book delivers a course module for advanced undergraduates, postgraduates and researchers of electronics, computing science, medical imaging, or wherever the study of identification and classification of objects by electronics-driven image processing and pattern recognition is relevant. Object analysis first uses image processing to detect objects and extract their features, then identifies and classifies them by pattern recognition. Its manifold applications include recognition of objects in satellite images which enable discrimination between different objects, such as fishing boats, merchant ships or warships; machine spare parts e.g. screws, nuts etc. (engineering); detection of cancers, ulcers, tumours and so on (medicine); and recognition of soil particles of different types (agriculture or soil mechanics in civil engineering). - Outlines the identification and classification of objects by electronics-driven image processing and pattern recognition - Discusses object detection, shape, roundness and sharpness analysis, orientation analysis and arrangement analysis - Delivers a course module for advanced undergraduates, postgraduates and researchers of electronics, computing science and medical imaging

The two-volume set LNCS 3522 and 3523 constitutes the refereed proceedings of the Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005, held in Estoril, Portugal in June 2005. The 170 revised full papers presented were carefully reviewed and selected from 292 submissions. The papers are organized in topical sections on computer vision, shape and matching, image and video processing, image and video coding, face recognition, human activity analysis, surveillance, robotics, hardware architectures, statistical pattern recognition, syntactical pattern recognition, image analysis, document analysis, bioinformatics, medical imaging, biometrics, speech recognition, natural language analysis, and applications.