"This monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labelling and classification. It is directed to the scientists, application engineers, professors, professors and students will find this book useful"--SpringerLink Title Preface Contents Recent Advances in Pattern Classification New Directions in Pattern Classification References Neural Networks for Handwriting Recognition Introduction State-of-the-Art Contribution Data Processing General Processing Steps Our Online System Our Offline System Neural Network Based Recognition Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks Connectionist Temporal Classification (CTC) Multidimensional Recurrent Neural Networks Hierarchical Subsampling Recurrent Neural Networks Experiments Comparison with HMMs on the IAM Databases Recognition Performance of MDLSTM on Contest’ Data Conclusion References Moving Object Detection from Mobile Platforms Using Stereo Data Registration Introduction Related Work Proposed Approach System Setup Feature Detection and Tracking Robust Registration Frame Subtraction Experimental Results Conclusions References Pattern Classifications in Cognitive Informatics Introduction Semantic Analysis Stages Semantic Analysis vs. Cognitive Informatics Example of a Cognitive UBIAS System Conclusions References Optimal Differential Filter on Hexagonal Lattice Introduction Preliminaries Least Inconsistent Image Point Spread Function Condition for Gradient Filter Numerical Optimization Theoretical Evaluation Signal-to-Noise Ratio Localization Experimental Evaluation Construction of Artificial Images Detection of Gradient Intensity and Orientation Overington’s Method of Orientation Detection Relationship between Derived Filter and Staunton Filter Experiment and Results Discussion Summary References Graph Image Language TechniquesSupporting Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations Introduction The Classification Problem Stages in the Analysis of CT Images under a Structural Approach Utilising Graph Techniques Parsing Languages Generated by Graph Grammars Picture Grammars in Classification and Semantic Interpretation of 3D Coronary Vessels Visualisations Characteristics of the Image Data Preliminary Analysis of 3D Coronary Vascularisation Reconstructions Graph-Based Linguistic Formalisms in Spatial Modelling of Coronary Vessels Detecting Lesions and Constructing the Syntactic Analyser Selected Results Conclusions Concerning the Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations References A Graph Matching Approach to Symmetry Detection and Analysis Introduction Symmetries and Their Properties Rotational Symmetry Reflectional Symmetry Interrelations between Rotational and Reflectional Symmetries Discussion Previous Work Previous Work in Symmetry Detection and Analysis Local Features Spectral Matching of Sets of Points in Rn Spectral Symmetry Analysis Spectral Symmetry Analysis of Sets in $R$n Spectral Symmetry Analysis of Images Experimental Results Symmetry Analysis of Images Statistical Accuracy Analysis Analysis of Three-Dimensional Symmetry Implementation Issues Additional Results Conclusions References Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps Introduction Interpretation of Perfusion Maps – Long and Short Time Prognosis Image Processing and Abnormality Detection Image Registration Affine Registration FFD Registration Thirion’s Demons Algorithm Comparison of Registration Algorithms Classification of Detected Abnormalities System Validation and Results Data Visualization – Augmented Reality Environment Augmented Reality Environment Real Time Rendering of 3D Data Augmented Desktop - System Performance Test Summary References Inference of Co-occurring Classes: Multi-class and Multi-label Classification Introduction Applications The Classification Process Data and Annotation Classification Approaches Binary Classification Multi-class Classification Multi-label Classification Multi-class Classification Multiple Binary classifiers Direct Multi-class Classification Associative Classification Multi-label Classification Semi-supervised (Annotation) Methods Inference of Co-occurring Affective States from Non-verbal Speech Summary References Author Index Annotation This monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labelling and classification. It is directed to the scientists, application engineers, professors, professors and students will find this book useful This volume describes the application of pattern recognition and classification approaches. Methods include handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, and more.
