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• Preface
• Research and Review Studies
• Visual Pattern Recognition: What Makes Faces so Special?
• Collision Recognition Inspired by Visual Neural Systems in Insects
• Advances and Challenges in 3D and 2D+3D Human Face Recognition
• Psychological Studies of the Criteria for 3D Free Form Shape Matching
• Procedural Learning Differences in Obsessive-Compulsive Disorder: A Bridge between Cognitive-Behavioral and Neuropsychological Models?
• Modeling of Ambulatory Heart Rate using Linear and Neural Network Approaches
• Neurophysical Correlates of Pattern Recognition in the Peripheral Visual Field (Pattern Recognition in the Peripheral Visual Field)
• Toll Bridge between the Outside World and Self: A Road to Safety or a Pass to Atherosclerosis?
• Index.
• (source: Nielsen Book Data)

Pattern recognition is the research area that studies the operation and design of systems that recognise patterns in data. It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. This book presents leading-edge research from around the world.
(source: Nielsen Book Data)

• Chapter 1. Revolutions in Infant fingerprint Recognition-A Survey.-
• Chapter 2. A Review of High Utility Itemset Mining for Transactional Database.-
• Chapter 3. A Cross-sectional study on distributed mutual exclusion algorithms for ad hoc networks.-
• Chapter 4. Electromagnetic Pollution Index Estimation of Green Mobile Communication of Macrocell.-
• Chapter 5. Prediction of Train delay System in Indian Railways using Machine Learning Techniques: Survey.-
• Chapter 6. Valence of emotion recognition using EEG.-
• Chapter 7. A Deep Learning-based Approach for Automated Brain Tumor Segmentation in MR images.-
• Chapter 8. MZI based Electro-Optic Reversible XNOR/XOR Derived from Modified Fredkin Gate.-
• Chapter 9. Secured Remote Access of Cloud Based Learning Management System (LMS) Using VPN.-
• Chapter 10. Surface EMG signal classification for hand gesture recognition.-
• Chapter 11. Improved Energy Efficiency in Street Lighting: A Coverage based Approach.-
• Chapter 12. Security and Challenges for Cognitive IOT Based Future City Architecture.-
• Chapter 13. A Heuristic Model for Friend Selection in Social Internet of Things.-
• Chapter 14. A Fuzzy string matching based reduplication with morphological attributes.-
• Chapter 15. Accelerating LOF Outlier Detection Approach. etc.
• (source: Nielsen Book Data)

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG). .
(source: Nielsen Book Data)

• Preface
• Notation
• Part I. Introduction and Overview: 1. Introduction
• 2. Mathematical background
• 3. Overview of a pattern recognition system
• 4. Evaluation
• Part II. Domain-Independent Feature Extraction: 5. Principal component analysis
• 6. Fisher's linear discriminant
• Part III. Classifiers and Tools: 7. Support vector machines
• 8. Probabilistic methods
• 9. Distance metrics and data transformations
• 10. Information theory and decision trees
• Part IV. Handling Diverse Data Formats: 11. Sparse and misaligned data
• 12. Hidden Markov model
• Part V. Advanced Topics: 13. The normal distribution
• 14. The basic idea behind expectation-maximization
• 15. Convolutional neural networks
• References
• Index.
• (source: Nielsen Book Data)

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.
(source: Nielsen Book Data)

• INTRODUCTORY ISSUES. Paradigmatic considerations on syntactic pattern recognition
• Methodology of syntactic pattern recognition ; STRING-BASED MODELS. Pattern recognition based on regular and CF grammars
• Enhanced string-based models for pattern recognition
• Inference (induction) of string languages
• Applications of string methods ; TREE-BASED MODELS. Pattern recognition based on tree languages
• Inference (induction) of tree languages
• Applications of tree methods ; GRAPH-BASED MODELS. Pattern analysis with graph grammars
• Inference (induction) of graph languages
• Applications of graph methods ; FUTURE OF SYNTACTIC PATTERN RECOGNITION. Summary of results and open problems
• Methodological issues.

This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.
(source: Nielsen Book Data)

• Preface
• Acknowledgements
• Part I. Analysis: 1. Probabilistic models
• 2. Exact string matching
• 3. Constrained exact string matching
• 4. Generalized string matching
• 5. Subsequence pattern matching
• Part II. Applications: 6. Algorithms and data structures
• 7. Digital trees
• 8. Suffix trees and Lempel-Ziv'77
• 9. Lempel-Ziv'78 compression algorithm
• 10. String complexity
• Bibliography
• Index.
• (source: Nielsen Book Data)

How do you distinguish a cat from a dog by their DNA? Did Shakespeare really write all of his plays? Pattern matching techniques can offer answers to these questions and to many others, from molecular biology, to telecommunications, to classifying Twitter content. This book for researchers and graduate students demonstrates the probabilistic approach to pattern matching, which predicts the performance of pattern matching algorithms with very high precision using analytic combinatorics and analytic information theory. Part I compiles known results of pattern matching problems via analytic methods. Part II focuses on applications to various data structures on words, such as digital trees, suffix trees, string complexity and string-based data compression. The authors use results and techniques from Part I and also introduce new methodology such as the Mellin transform and analytic depoissonization. More than 100 end-of-chapter problems help the reader to make the link between theory and practice.
(source: Nielsen Book Data)

• Keynote Paper.- Visual Information Retrieval - Future Directions and Grand Challenges.- Image and Video Retrieval.- Approximation-Based Keypoints in Colour Images - A Tool for Building and Searching Visual Databases.- A Knowledge Synthesizing Approach for Classification of Visual Information.- Image Similarity - From Fuzzy Sets to Color Image Applications.- A Semi-automatic Feature Selecting Method for Sports Video Highlight Annotation.- Face Image Retrieval System Using TFV and Combination of Subimages.- Near-Duplicate Detection Using a New Framework of Constructing Accurate Affine Invariant Regions.- Where Are Focused Places of a Photo?.- Region Based Image Retrieval Incorporated with Camera Metadata.- Empirical Investigations on Benchmark Tasks for Automatic Image Annotation.- Automatic Detection and Recognition of Players in Soccer Videos.- A Temporal and Visual Analysis-Based Approach to Commercial Detection in News Video.- Salient Region Filtering for Background Subtraction.- A Novel SVM-Based Method for Moving Video Objects Recognition.- Image Classification and Indexing by EM Based Multiple-Instance Learning.- Visual Biometrics.- Palm Vein Extraction and Matching for Personal Authentication.- A SVM Face Recognition Method Based on Optimized Gabor Features.- Palmprint Identification Using Pairwise Relative Angle and EMD.- Finding Lips in Unconstrained Imagery for Improved Automatic Speech Recognition.- Intelligent Visual Information Processing.- Feature Selection for Identifying Critical Variables of Principal Components Based on K-Nearest Neighbor Rule.- Denoising Saliency Map for Region of Interest Extraction.- Cumulative Global Distance for Dimension Reduction in Handwritten Digits Database.- A New Video Compression Algorithm for Very Low Bandwidth Using Curve Fitting Method.- The Influence of Perceived Quality by Adjusting Frames Per Second and Bits Per Frame Under the Limited Bandwidth.- An Evolutionary Approach to Inverse Gray Level Quantization.- Visual Data Mining.- Mining Large-Scale News Video Database Via Knowledge Visualization.- Visualization of the Critical Patterns of Missing Values in Classification Data.- Visualizing Unstructured Text Sequences Using Iterative Visual Clustering.- Enhanced Visual Separation of Clusters by M-Mapping to Facilitate Cluster Analysis.- Multimedia Data Mining and Searching Through Dynamic Index Evolution.- Ubiquitous and Mobile Visual Information Systems.- Clustering and Visualizing Audiovisual Dataset on Mobile Devices in a Topic-Oriented Manner.- Adaptive Video Presentation for Small Display While Maximize Visual Information.- An Efficient Compression Technique for a Multi-dimensional Index in Main Memory.- RELT - Visualizing Trees on Mobile Devices.- Auto-generation of Geographic Cognitive Maps for Browsing Personal Multimedia.- Semantics.- Automatic Image Annotation for Semantic Image Retrieval.- Collaterally Cued Labelling Framework Underpinning Semantic-Level Visual Content Descriptor.- Investigating Automatic Semantic Processing Effects in Selective Attention for Just-in-Time Information Retrieval Systems.- News Video Retrieval by Learning Multimodal Semantic Information.- 2D/3D Graphical Visual Data Retrieval.- Visualization of Relational Structure Among Scientific Articles.- 3D Model Retrieval Based on Multi-Shell Extended Gaussian Image.- Neurovision with Resilient Neural Networks.- Applications of Visual Information Systems.- Visual Information for Firearm Identification by Digital Holography.- GIS-Based Lunar Exploration Information System in China.- Semantic 3D CAD and Its Applications in Construction Industry - An Outlook of Construction Data Visualization.- A Fast Algorithm for License Plate Detection.- Applying Local Cooccurring Patterns for Object Detection from Aerial Images.- Enticing Sociability in an Intelligent Coffee Corner.- Geometric and Haptic Modelling of Textile Artefacts.- A Toolkit to Support Dynamic Social Network Visualization.- The Predicate Tree - A Metaphor for Visually Describing Complex Boolean Queries.- Potentialities of Chorems as Visual Summaries of Geographic Databases Contents.- Compound Geospatial Object Detection in an Aerial Image.- Texture Representation and Retrieval Using the Causal Autoregressive Model.- An Approach Based on Multiple Representations and Multiple Queries for Invariant Image Retrieval.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Visual Information Systems, VISUAL 2007, held in Shanghai, China, in June 2007. The papers are organized in topical section on image and video retrieval, visual biometrics, intelligent visual information processing, visual data mining, ubiquitous and mobile visual information systems, semantics, 2D/3D graphical visual data retrieval, and applications of visual information systems.
(source: Nielsen Book Data)

• Oral - I.- Learning Personal Specific Facial Dynamics for Face Recognition from Videos.- A New Probabilistic Model for Recognizing Signs with Systematic Modulations.- Model-Based Stereo with Occlusions.- View Invariant Head Recognition by Hybrid PCA Based Reconstruction.- Poster - I.- Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models.- Automatic Facial Expression Recognition Using Boosted Discriminatory Classifiers.- Generating Body Surface Deformation Using Level Set Method.- Patch-Based Pose Inference with a Mixture of Density Estimators.- Integrating Multiple Visual Cues for Robust Real-Time 3D Face Tracking.- Model-Assisted 3D Face Reconstruction from Video.- Human Perambulation as a Self Calibrating Biometric.- Oral - II.- Detecting, Localizing and Classifying Visual Traits from Arbitrary Viewpoints Using Probabilistic Local Feature Modeling.- Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.- Structured Ordinal Features for Appearance-Based Object Representation.- SODA-Boosting and Its Application to Gender Recognition.- Poster - II.- Single Image Subspace for Face Recognition.- Human Face Processing with 1.5D Models.- Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition.- A Unified Framework of Subspace and Distance Metric Learning for Face Recognition.- Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion.- Towards Pose-Invariant 2D Face Classification for Surveillance.- Robust Face Recognition Strategies Using Feed-Forward Architectures and Parts.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2007, held within the scope of ICCV 2007, the International Conference on Computer Vision. The papers review the status of recognition, analysis and modeling of face, gesture, activity, and behavior. Topics addressed include feature representation, 3D face, video-based face recognition, facial motion analysis, and sign recognition.
(source: Nielsen Book Data)