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• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

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).
(source: Nielsen Book Data)

• Dimensionality reduction techniques for interactive visualization, exploratory data analysis and classification, A. Konig
• feature selection by artificial neural network for pattern classification, B. Chakraborty
• a new clustering with estimation of cluster number based on genetic algorithm, K. Imai et al
• minimizing the measurement cost in the classification of new samples by neural-network-based classifiers, H. Ishibuchi and M. Nii
• extraction of fuzzy rules from numerical data for classifiers, N.R. Pal and A. Sarkar
• a texture image segmentation method using neural networks and binary features, J. Zhang and S. Oe
• image retrieval system based on subjective information, K Yoshida et al. (Part contents).
• (source: Nielsen Book Data)

Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realise the system.A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subjective information. The other interesting aspect of the volume is that it covers all three major facets of soft computing: fuzzy logic, neural networks and evolutionary computing.
(source: Nielsen Book Data)

• Introduction
• Classifiers Based on Bayes Decision Theory
• Linear Classifiers
• Nonlinear Classifiers
• Feature Selection
• Feature Generation I
• Feature Generation II
• Template Matching
• Context-Dependant Classification
• System Evaluation
• Clustering: Basic Concepts Clustering Algorithms I (Sequential)
• Clustering Algorithms II (Hierarchical)
• Clustering Algorithms III (Functional Optimization)
• Clustering Algorithms IV (Graph Theory)
• Cluster Validity.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Retinal Image Analysis Approach for Diabetic Retinopathy Grading.- Mask R-CNN-based system for automated reindeer recognition and counting from aerial photographs.- Smart Tiling for Program Optimization and Parallelization.- Classification of Histology Images based on a Compact 3D Representation.- Authentication System Based on Biometric Data of Smiling Face from Stacked Autoencoder and Concatenated Reed-Solomon Codes.- Formalization of people and crowd detection and tracking for smart video surveillance.- Predicting events by analyzing the results of the work of predictive models.- Digest of Blockchain Technologies to Design System for Big Image Data Provenance and Security.- Generative Autoencoders for Designing Novel Small-Molecule Compounds As Potential Sars-COV-2 Main Protease Inhibitors.- Comparison of data preprocessing algorithms for nuclear segmentation in fluorescent immunohistology images of cancer cells.- A Bottom-up Method for Pose Detection of Multiple People on Real-Time Video.- Formalisation of Motion description in Microscopy Images.- Detection of features regions of Syndrome in Multiple Sclerosis on MRI.- Automatic tuning of the motion control system of a mobile robot along a trajectory based on the reinforcement learning method.- Simulation Modelling And Machine Learning Platforfm For Processing Fluorescence Spectroscopy Data.- Comparing the Performance of Classical and Deep Learning Methods on Small Image Datasets.- Investigation of the GAN-SSL Classifier Properties for Identification Expertise.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Conference on Pattern Recognition and Information Processing, PRIP 2021, held in Minsk, Belarus, in September 2021. Due to the COVID-19 pandemic the conference was held online. The 17 revised full papers were carefully reviewed and selected from 90 submissions. The papers present a discussion on theoretical and applied aspects of computer vision, recognition of signals and images, the use of distributed resources, and high-performance systems.
(source: Nielsen Book Data)

• Reproduced Papers.org: Openly Teaching and Structuring Machine Learning Reproducibility
• Reproducibility: Evaluating the Evaluations
• torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
• Spatio-Temporal Convolutional Autoencoders for Perimeter Intrusion Detection
• Creating Emotion Recognition Algorithms Based on a Convolutional Neural Network for Sentiment Analysis
• Tree Defect Segmentation Using Geometric Features and CNN
• Pith Estimation on Tree Log End Images
• Structure and Concept of the Benchmark for Vesselness Filters with Focus on Reproducibilty and Future Evaluations
• A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes
• On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
• Reproducibility Aspects of Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
• Reproducing the Sparse Huffman Address Map Compression for Deep Nural Networks
• Implementation of Genetic Pseudo Rehearsal.

This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Reproducible Research in Pattern Recognition, RRPR 2021, held as a virtual event, in January 2021. The 8 revised full papers, presented together with 6 short papers, were carefully reviewed and selected from 18 submissions. The papers were organized into three main categories. The first contributions focused on reproducible research frameworks. The second category focused on reproducible research results and the last category included ICPR companion papers describing implementation and details that are an absolute requirement for reproducibility.

• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

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).
(source: Nielsen Book Data)

• Introduction
• Classifiers Based on Bayes Decision Theory
• Linear Classifiers
• Nonlinear Classifiers
• Feature Selection
• Feature Generation I
• Feature Generation II
• Template Matching
• Context-Dependant Classification
• System Evaluation
• Clustering: Basic Concepts Clustering Algorithms I (Sequential)
• Clustering Algorithms II (Hierarchical)
• Clustering Algorithms III (Functional Optimization)
• Clustering Algorithms IV (Graph Theory)
• Cluster Validity.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Pattern recognition - evolution of methodologies and data mining, A. Pal and S.K. Pal
• adaptive stochastic algorithms for pattern classification, M.A.L. Thathachar and P.S. Sastry
• shape in images, K.V. Mardia
• decision trees for classification - a review and some new results, R. Kothari and M. Dong
• syntactic pattern recognition, A.K. Majumder and A.K. Ray
• fuzzy sets as a logic canvas for pattern recognition, W. Pedrycz and N. Pizzi
• neural network based pattern recognition, V. David Sanchez
• networks of spiking neurons in data mining, K. Cios and D.M. Sala
• genetic algorithms, pattern classification and neural networks design, S. Bandyopadhyay et al
• rough sets in pattern recognition, A. Skowron and R. Swiniarski
• automated generation of qualitative representations of complex objects by hybrid soft-computing methods, E.H. Ruspini and I.S. Zwir)
• writing speed and writing sequence invariant on-line handwriting recognition, S-H Cha and S.N. Srihari
• tongue diagnosis based on biometric pattern recognition technology, K. Wang et al
• and other papers.
• (source: Nielsen Book Data)

This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.
(source: Nielsen Book Data)

• Generalized Fuzzy Rough Sets Based on New Fuzzy Similarity Relation.- SV-NET: A Deep Learning Approach To Video Based Human Activity Recognition.- Machine Learning based Framework for Recognizing Traffic Signs on Road Surfaces.- Cursor Control Using Face Gestures.- A Smart Discussion Forum Website.- Certificate Management System Using Blockchain.- Reality Check in Virtual Space for Privacy Behavior of Indian Users of Social Networking Sites.- Application of artificial electric field algorithm for economic load dispatch problem.- Intelligent Data Compression Policy for Hadoop Performance Optimization.
• (source: Nielsen Book Data)

This book highlights recent research on soft computing, pattern recognition and biologically inspired computing. It presents 24 selected papers from the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) and 5 papers from the 11th World Congress on Nature and Biologically Inspired Computing (NaBIC 2019), held at Vardhaman College of Engineering, Hyderabad, India, on December 13-15, 2019. SoCPaR-NaBIC is a premier conference and brings together researchers, engineers and practitioners whose work involves soft computing and bio-inspired computing, as well as their industrial and real-world applications. Including contributions by authors from 15 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
(source: Nielsen Book Data)

• Face Recognition Applications.- Skin-based Face Detection-Extraction and Recognition of Facial Expressions.- Facial Image Processing.- Face Recognition and Pose Estimation with Parametric Linear Subspaces.- Spatio-Temporal Patterns.- 4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors.- Measuring Similarity Between Trajectories of Mobile Objects.- Graph-Based Methods.- Matching of Hypergraphs - Algorithms, Applications, and Experiments.- Feature-Driven Emergence of Model Graphs for Object Recognition and Categorization.- Special Applications.- A Wavelet-based Statistical Method for Chinese Writer Identification.- Texture Analysis by Accurate Identification of a Generic Markov-Gibbs Model.
• (source: Nielsen Book Data)

A sharp increase in the computing power of modern computers has triggered the development of powerful algorithms that can analyze complex patterns in large amounts of data within a short time period. Consequently, it has become possible to apply pattern recognition techniques to new tasks. The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
(source: Nielsen Book Data)

• Chapter 1 Introduction
• Chapter 2 Context-aware semi-local feature detector
• Chapter 3 Unsupervised vocabulary optimization
• Chapter 4 Supervised vocabulary learning
• Chapter 5 3D Visual Descriptor Learning.
• (source: Nielsen Book Data)

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.
(source: Nielsen Book Data)

• Graph-Based Representation and Recognition
• Matching Hierarchies of Deformable Shapes
• Edition within a Graph Kernel Framework for Shape Recognition
• Coarse-to-Fine Matching of Shapes Using Disconnected Skeletons by Learning Class-Specific Boundary Deformations
• An Optimisation-Based Approach to Mesh Smoothing: Reformulation and Extensions
• Graph-Based Representation of Symbolic Musical Data
• Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods
• Computing and Visualizing a Graph-Based Decomposition for Non-manifold Shapes
• A Graph Based Data Model for Graphics Interpretation
• Tracking Objects beyond Rigid Motion
• Graph-Based Registration of Partial Images of City Maps Using Geometric Hashing
• Graph Matching
• A Polynomial Algorithm for Submap Isomorphism
• A Recursive Embedding Approach to Median Graph Computation
• Efficient Suboptimal Graph Isomorphism
• Homeomorphic Alignment of Edge-Weighted Trees
• Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors
• Graph Matching Based on Node Signatures
• A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs
• Arc-Consistency Checking with Bilevel Constraints: An Optimization
• Graph Clustering and Classification
• Pairwise Similarity Propagation Based Graph Clustering for Scalable Object Indexing and Retrieval
• A Learning Algorithm for the Optimum-Path Forest Classifier
• Improving Graph Classification by Isomap
• On Computing Canonical Subsets of Graph-Based Behavioral Representations
• Object Detection by Keygraph Classification
• Graph Regularisation Using Gaussian Curvature
• Characteristic Polynomial Analysis on Matrix Representations of Graphs
• Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering
• Pyramids, Combinatorial Maps, and Homologies
• Irregular Graph Pyramids and Representative Cocycles of Cohomology Generators
• Annotated Contraction Kernels for Interactive Image Segmentation
• 3D Topological Map Extraction from Oriented Boundary Graph
• An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts
• A First Step toward Combinatorial Pyramids in n-D Spaces
• Cell AT-Models for Digital Volumes
• From Random to Hierarchical Data through an Irregular Pyramidal Structure
• Graph-Based Segmentation
• Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation
• Texture Segmentation by Contractive Decomposition and Planar Grouping
• Image Segmentation Using Graph Representations and Local Appearance and Shape Models
• Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework.

This book constitutes the refereed proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2009, held in Venice, Italy in May 2009. The 37 revised full papers presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on graph-based representation and recognition, graph matching, graph clustering and classification, pyramids, combinatorial maps, and homologies, as well as graph-based segmentation.
(source: Nielsen Book Data)

• Contents: Introduction to ADIAC and This Book (H Du Buf & M M Bayer)
• Diatoms: Organism and Image (D G Mann)
• Diatom Applications (R J Telford et al.)
• ADIAC Imaging Techniques and Databases (M M Bayer & S Juggins)
• Human Error and Quality Assurance in Diatom Analysis (M G Kelly et al.)
• Contour Extraction (S Fischer et al.)
• Identification Using Classical and New Features in Combination with Decision Tree Ensembles (S Fischer & H Bunke)
• Identification by Curvature of Convex and Concave Segments (R E Loke & H du Buf)
• Identification by Contour Profiling and Legendre Polynomials (A Ciobanu & H du Buf)
• Identification by Gabor Features (L M Santos & H du Buf)
• Identification by Mathematical Morphology (M H F Wilkinson et al.)
• Mixed-Method Identifications (M A Westenberg & J B T M Roerdink)
• Automatic Slide Scanning (J L Pech-Pacheco & G Cristobal)
• ADIAC Achievements and Future Work (H du Buf & M M Bayer).
• (source: Nielsen Book Data)

This is the first book to deal with automatic diatom identification. It provides the necessary background information concerning diatom research, useful for both diatomists and non-diatomists. It deals with the development of electronic databases, image preprocessing, automatic contour extraction, the application of existing contour and ornamentation features and the development of new ones, as well as the application of different classifiers (neural networks, decision trees, etc.). These are tested using two image sets: (i) a very difficult set of Sellaphora pupula with 6 demes and 120 images; (ii) a mixed genera set with 37 taxa and approximately 800 images. The results are excellent, and recognition rates well above 90% have been achieved on both sets. The results are compared with identification rates obtained by human experts. One chapter of the book deals with automatic image capture, i.e. microscope slide scanning at different resolutions using a motorized microscope stage, autofocusing, multifocus fusion, and particle screening to select only diatoms and to reject debris. This book is the final scientific report of the European ADIAC project (Automatic Diatom Identification and Classification), and it lists the web-sites with the created public databases and an identification demo.
(source: Nielsen Book Data)

• From the contents: Support Vector Machines - An Introduction.- Multiple Model Estimation for Nonlinear Classification.- Componentwise Least Squares Support Vector Machines.- Active Support Vector Learning with Statistical Queries.- Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine.- Active-Set Methods for Support Vector Machines.- Theoretical and Practical Model Selection Methods for Support Vector Classifiers.- Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification.- Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods.- An Accelerated Robust Support Vector Machine Algorithm.- Fuzzy Support Vector Machines with Automatic Membership Setting.- Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance.- Kernel Discriminant Learning with Application to Face Recognition.- Fast Color Texture-based Object Detection in Images: Application to License Plate Localization.
• (source: Nielsen Book Data)

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.
(source: Nielsen Book Data)

• 1. Introduction.-
• 2. Optimal Classification.-
• 3. Sample-Based Classification.-
• 4. Parametric Classification.-
• 5. Nonparametric Classification.-
• 6. Function-Approximation Classification.-
• 7. Error Estimation for Classification.-
• 8. Model Selection for Classification.-
• 9. Dimensionality Reduction.-
• 10. Clustering.-
• 11. Regression.- Appendix.-.
• (source: Nielsen Book Data)

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website. .
(source: Nielsen Book Data)

• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

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).
(source: Nielsen Book Data)

• Performance characterization in computer vision.- Low-level computational mono and stereo vision: A Bayesian approach.- Topographic structure of image.- The dual irregular pyramid.- Analytical results on the Quadtree storage-requirements.- From pyramids to quadtrees: Approximation of heterogeneous surfaces by fixing complexity.- Calculation and estimation of sample statistics of binary images using quadtree data representations.- Temporal speckle reduction for feature extraction in ultrasound images.- Noise effects in statistical subpixel pattern recognition.- Fast iterative reconstruction of band-limited images from non-uniform sampling values.- Anisotropic filtering of MRI data based upon image gradient histogram.- Fast discrete cosine transform approximation for JPEG image compression.- Error diffusion in Block Truncation Coding.- On a bound on signal-to-noise ratio in subband coding of Gaussian image process.- A linear predictor as a regularization function in adaptive image restoration and reconstruction.- Inversion of convolution by small kernels.- A model-based image quantization technique for supervised image recognition.- Brightness-contrast diffusion and the grouping of missing angles.- Rotation invariance in edge detection.- Using eigenvectors of a vector field for deriving a second directional derivative operator for color images.- Crest lines detection in grey level images: Studies of different approaches and proposition of a new one.- Contour - Detection using the shape of the nearest neighbors Set (NN-Set).- A comparative study of performance for noisy roof edge detection.- A Hough-like prediction/correction approach for ellipse detection.- Fourier parameterization provide uniform bounded Hough Space.- Motion estimation and the Randomized Hough Transform (RHT): New methods with gradient information.- Circle extraction via least squares and the Kalman filter.- A multiresolution shape description algorithm.- Image coding by morphological skeleton transformation.- A non-linear shape abstraction technique.- Minimum-space time-optimal convex hull algorithms (preliminary report).- Detecting corners of polygonal and polyhedral objects.- A fast algorithm for dominant point detection on chain-coded contours.- Shapes and metrics.- Simplified technique of structure extraction from textural images.- Adaptive pyramid approach to texture segmentation.- Markov random fields with short- and long-range interaction for modelling gray-scale textured images.- Texture recognition by the q-th order fractal analysis.- A proximity measure of line drawings for comparison of chemical compounds.- Jigsaw puzzle solving using approximate string matching and best-first search.- A morphological approach to the generalised 2-stage stock-cutting problem.- Primitive and compound patterns.- A similarity measure between 3-D objects and its parallel computation.- Automated learning of rules using genetic operators.- Symbolic and iconic information combination for satellite imagery interpretation.- Multi-class classification and symbolic cognitive processing with ALISA.- Computing image flow using a coarse-to-fine strategy for spatiotemporal filters.- Visual motion estimation from image contour tracking.- Robust recovery of ego-motion.- A temporal smoothing technique for real-time motion detection.- Combined evaluation of motion and disparity vector fields for stereoscopic sequence coding.- Recovering translational motion parameters from image sequences using Randomized Hough Transform.- Estimating optical flow for large interframe displacements.- Surface discontinuities in range images.- Algorithms for shape from shading, lighting direction and motion.- Separating diffuse and specular component of image irradiance by translating a camera.- Necessary and sufficient conditions for a unique solution of plane motion and structure.- Photometric stereo for non-lambertian surfaces using color information.- Active fixation for junction classification.- Point matching for registration of free-form surfaces.- Temporal precedence in asynchronous visual indexing.- Improved stripe matching for colour encoded structured light.- Fusion of the stereoscopic and temporal matching results by an algorithm of coherence control and conflicts management.- 3D Model based stereo reconstruction using coupled Markov random fields.- Occlusions and special views within the reconstruction of polyhedral scenes.- Fast shadowing of volume data.- Three dimensional moment invariants under rigid transformation.- Parameterisation of simple geometrical lambertian surfaces using photometric stereo.- Hough transform to extract 3D information from images of different viewpoints.- Fast algorithm for the stereo pair matching with parallel computation.- Dense stereo correspondence using polychromatic block matching.- Planning the next view using the max-min principle.- Cross-correlation with reconstruction: A new approach to pattern matching.- The iterated normalized backprojection method of image reconstruction.- Cone beam reconstruction and fourier transform of distributions.- Character recognition by affine moment invariants.- Ultra fast pattern classification by fuzzy logic.- An object-oriented pen-based recognizer for handprinted characters.- On the method of critical points in character recognition.- Printed text segmentation using distance transform.- Recognition of handwritten layout drawings.- Automatic recognition of scanned technical drawings.- Digital image processing in radiology: An experience of development of technology for computer-aided diagnosis.- Modeling and quantification of protein maps by Gaussian fitting.- Computer analysis of the large intestine contours for the recognition of diseases.- A rule-based approach to hand X-ray image segmentation.- 3-D cerebral vessel reconstruction from angiograms.- Comparison of different approaches to suppress speckles in ultrasonic tomograms.- A test-bed for computer-assisted fusion of multi-modality medical images.- Object-oriented volume segmentation.- Smooth morphological transformation of CT and MR medical data.- Evaluation of plaque formation - Surface reflectance measurement.- Analysis of MR angiography volume data leading to the structural description of the cerebral vessel tree.- The automatic classification of normal and abnormal chromosomes using image analysis.- Real time image processing for fast seam tracking.- An experimental vision tool for real time quality control.- High-tech approaches of computer vision in industry.- An image processing system for material flow control in coal mining industry.- Transparency quantification. Application to composite materials quality controls by image processing.- Control of composite material strucrure by fractal methods.- Model-based adaptive preprocessing of images in automatic visual inspection.- Application of knowledge-based image inspection system for diagnosis of misprints in offsetprinting.- Multiple image matching in an automatic aerotriangulation system.- Digital processing of skylab X-ray images of the solar corona..- Finding human faces in a picture.- CADI: Computer assisted educational package for digital image processing.- Computer image analysis to locate targets for an agricultural robot.- The application of digital image processing in the evaluation of agricultural experiments.- GCV-Aided linear image regularization for the reconstruction of wave distribution function of magnetospheric VLF/ELF waves.- Crater detection in aero-space imagery using simple nonparametric statistical tests.- Digital retina simulating dynamic behavior of visual perception.- A new heterogeneous and reconfigurable architecture for image analysis.- The analogic single-chip CNN visual supercomputer - a review.- A variant of learning vector quantizer based on split-merge statistical tests.- Neural networks classifiers based on geocoded data and multispectral images for satellite image interpretation.- Artificial neural networks for image improvement.- Using Cellular Neural Network to "See" random-dot stereograms.
• (source: Nielsen Book Data)

This volume constitutes the proceedings of the 5th International Conference on Computer Analysis of Images and Patterns (CAIP'93), held in Budapest, Hungary, in September 1993. Formerly, the events in this biennial conference series were thought as a forum where East European researchers and professionals from academia and industry had an opportunity to discuss their results and ideas with Western colleagues active in image processing and pattern recognition. Now, CAIP'93 has a much more international scope, and in the future these conferences will not any longertake place only in East European countries, but roam throughout whole Europe. Besides invited talks by Belikova, Gimel'farb, Haralick and Roska, the volume contains 114 contributions, either presented as lectures or posters and carefully selected by a highly competent international program committee from a total of some 230 submissions; thus the book gives a thorough survey on recent research results and their applications in image processing and pattern recognition. The proceedings is organized in 20 sections, for example on image data structures, image processing, edges and contours, Hough transforms and related methods, shape, motion, 3-D vision, character recognition and document processing, biomedical applications, industrial applications, and neural networks.
(source: Nielsen Book Data)

• Chapter 1 Introduction to Computer Vision and Deep Learning.-
• Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3 Image Classification using LeNet.-
• Chapter 4 VGGNet and AlexNext Networks.-
• Chapter 5 Object Detection Using Deep Learning.-
• Chapter 6 Facial Recognition and Gesture Recognition.-
• Chapter 7 Video Analytics Using Deep Learning.-
• Chapter 8 End-to-end Model Development.- Appendix.
• (source: Nielsen Book Data)

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop on Visual Form, IWVF-4, held in Capri, Italy, in May 2001. The 66 revised full papers presented together with seven invited papers were carefully reviewed and selected from 117 submissions. The book covers theoretical and applicative aspects of visual form processing. The papers are organized in topical sections on representation, analysis, recognition, modelling and retrieval, and applications.
(source: Nielsen Book Data)