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This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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Image segmentation. --- Cluster analysis. --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC). The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP). Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM). Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques’ performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.

Electrical engineering. --- Optical data processing. --- Computational intelligence. --- Algorithms. --- Communications Engineering, Networks. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Computational Intelligence. --- Algorism --- Algebra --- Arithmetic --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Electric engineering --- Engineering --- Foundations --- Optical equipment --- Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author’s core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data. Contents Deformable Meshes for Accurate Automatic Segmentation Omnidirectional Displacements for Deformable Surfaces (ODDS) Coupled Deformable Surfaces for Multi-object Segmentation From Surface Mesh Deformations to Volume Deformations Segmentation of Anatomical Structures in Medical Image Data    Target Groups Academics and practitioners in the fields of computer science, medical imaging, and automatic segmentation.  The Author Dagmar Kainmueller works as a research scientist at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, with a focus on bio image analysis.  The Editor The series Aktuelle Forschung Medizintechnik – Latest Research in Medical Engineering is edited by Thorsten M. Buzug.  .

Computer Science. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Biomedical Engineering. --- Computer science. --- Computer vision. --- Biomedical engineering. --- Informatique --- Vision par ordinateur --- Génie biomédical --- Diagnostic imaging -- Digital techniques. --- Image segmentation. --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Technology - General --- Electrical Engineering --- Applied Physics --- Diagnostic imaging --- Digital techniques. --- Machine vision --- Vision, Computer --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Digital diagnostic imaging --- Computer graphics. --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Image analysis --- Digital electronics --- Digital techniques --- Biomedical Engineering and Bioengineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.

Engineering. --- Artificial intelligence. --- Computational Intelligence. --- Artificial Intelligence. --- Signal, Image and Speech Processing. --- Computational intelligence. --- Signal processing. --- Image processing. --- Speech processing systems. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques