Over the last two decades, major advances in ovulation induction and assisted reproductive technologies have been made, and now, in the 1990s, the aim is to concentrate talent and efforts on the next obvious step - achieving a high rate of successful full-term pregnancies by concentrating on implantation and early pregnancy. Investigating and surmounting the disorders associated with this critical period are the major hurdles, which, through research and clinical efforts, must be overcome in order to achieve this goal.
Leading workers in the field of human reproduction provide both basic knowledge and useful practical information in this book about the most critical phase in the development of a new human being: the first twelve weeks of life. The period from fertilization and implantation to the end of the first trimester is still insufficiently understood and marks a new frontier. The knowledge necessary for applying our present diagnostic capabilities and for venturing into the problematic areas of fertilization technology and embryonic treatment is made easily available in this comprehensive textbook. The book is divided into two parts. The first presents basic information about physiology, anatomy, in vivo investigations, biochemistry and legal aspects. The second part focuses on pregnancy development, monitoring and the clinical diagnosis and management of disorders in the early stages of life. A full section is devoted to assisted conception and the newest possibilities in fertilizationtechnologies, whereby the ethical aspects are also discussed.
Over the last two decades, major advances in ovulation induction and assisted reproductive technologies have been made, and now, in the 1990s, the aim is to concentrate talent and efforts on the next obvious step - achieving a high rate of successful full-term pregnancies by concentrating on implantation and early pregnancy. Investigating and surmounting the disorders associated with this critical period are the major hurdles, which, through research and clinical efforts, must be overcome in order to achieve this goal.
Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms. Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions. This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.
Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/ Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications—including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians—give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples—code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)
Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject
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